MyAssays Desktop User'

July 2017
MyAssays Desktop
User's Guide
www.myassays.com
Copyright Notice
Copyright © MyAssays Ltd. 2017.
All rights reserved.
This documentation and the MyAssays Desktop software products are copyrighted
MyAssays Ltd. 2017.
The documentation and the MyAssays Desktop software products are protected by
copyright throughout the world and must not be reproduced in whole or in part in any
manner, including in print, online, or by other electronic means, without permission in
writing of MyAssays Ltd.
MyAssays Ltd. gives no warranties and makes no representations about the
documentation contents or the MyAssays Desktop software products and specifically
disclaims any implied warranties or merchantability or fitness for any purpose.
MyAssays Ltd. reserves the right to revise the documentation and the software
products and to make changes in their content from time to time without notifying any
person of such revisions or changes.
The MyAssays name and logo is a registered trademark of MyAssays Ltd.
Additional 3rd Party Software Licensing Notices are provided.
MyAssays Desktop
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Table of Contents
Introducing MyAssays Desktop
7
How to Use This Guide
8
Features
9
Product Editions
Getting Started
Installation
17
20
20
System Requirements
20
Installation Process
21
Launching
22
Product Licensing
23
License Activation
24
Licensed Features
26
License Deactivation
27
Activate New License
29
Update Existing License
30
Quick Start
31
MyAssays Desktop – The Basics
31
Introducing MyAssays Desktop Explorer
32
Introducing MyAssays Analysis
34
Video Tutorials
Working with MyAssays Desktop
39
41
Key Concepts
41
Getting Around
46
MyAssays Desktop Explorer
47
Layout Editor
60
Analysis
65
New Protocol Wizard
70
Analysis Launcher
71
Common Tasks
72
MyAssays Desktop
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Data Storage
72
Data Import
72
Visualizing Data
72
Flagging/Outlier Exclusion
77
Interactive Flagging
77
Calculated Flagging
81
Flagged Data in the Report
82
Flagged Data in Text-Based Exports
83
Sample Labels
90
Validation
94
Evaluation
100
Report Content
104
Reference
Transforms
Endpoint Transforms
141
141
144
Auto Flag
147
Average
149
Blank Correction
151
Dilution Curves
158
Dilution Factors
163
Expression by Matrix (Dual)
167
Expression by Matrix
169
Expression by Position
171
Factor
174
Matrix Difference
176
Median
178
Percentage Coefficient of Variation
180
Percentage
183
Replicate Outlier Removal
185
Standard Curve Fit
188
MyAssays Desktop
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Standard Deviation
195
Standard Error of Mean
197
Variance
200
XY Transforms
204
XY Baseline Correction
206
XY Blank Correction
208
XY Expression (Dual)
212
XY Expression
213
XY Fit
214
XY Join
215
XY Outlier Removal
216
XY Replicates Average
217
XY Reduction Transforms
219
XY Reduction Average
221
XY Reduction Fit Parameter
223
XY Reduction Maximum Slope
224
XY Reduction Peak
226
XY Reduction Total
227
XY Reduction X At Y
228
XY Reduction X Between Y
229
XY Reduction Y At X
230
Curve Fitting
231
Methods
231
Goodness of Fit Measures
234
Best Fit
235
Standard Curve Fit Advanced Configuration
240
Expressions
247
Integration with Readers
272
Command Line Arguments
280
Tweak Files
284
MyAssays Desktop
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Installing MyAssays Desktop Alongside WorkOut
286
Referencing MyAssays Desktop
287
Additional Software Licensing Notices
288
MyAssays Desktop
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Introducing MyAssays Desktop
MyAssays Desktop is an easy-to-use data analysis tool for bioassays.
MyAssays Desktop is the only desktop tool with an integrated search-and-download feature
providing direct access to the growing online database of protocols from myassays.com. The
preconfigured data analysis protocols cover popular applications and support for commercial
assay kits (backed by leading kit manufacturers); these are all ready to download and
integrate into your lab’s workflow.
Beyond this, if your application is not already supported, the software provides a powerful yet
easy-to-use system to build and customize your own data analysis protocols.
MyAssays Desktop provides first-class tools for:
l Data Import
l Data Visualization
l Data Analysis (including advanced Curve Fitting)
l Reporting
l Export
l Workflow Integration
MyAssays Desktop User Guide
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How to Use This Guide
We have arranged this guide to help you get started quickly and to simplify how you can find
the information and details that you need.
This Introduction contains a summary of the key features and editions of the software.
The Getting Started section covers installation and general orientation information. It also
includes a Quick Start section to show you where the basics are and to walk you through your
first data analysis. It also provides links to further information and online video guides.
The Working with MyAssays Desktop section dives into further detail, covering each
element in more detail with the content arranged into logical sections. This includes:
l getting around the various applications
l protocols
l common tasks (such as importing, exporting, curve fitting, marking outliers to exclude, and
much more)
The final Reference section provides a more complete description of each facet. Here the
content is arranged to simplify quick retrieval.
What Should I Read?
We encourage all new users to read through all of the Introduction and Getting Started
sections.
You will find more information about the topics and get a better understanding from the
Working with MyAssays Desktop section. Feel free to skip sections here that are not
relevant to your applications. If you’re working with particular protocols or need to perform
specific tasks, be sure to follow the relevant sections under Protocols and Common Tasks.
We don’t expect you to read through the Reference section in order; it is more useful to
consider it a resource for providing further details as and when you need them.
MyAssays Desktop User Guide
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Features
Supported Measurements
MyAssays Desktop is designed to process measurement data from any microplate or rackbased reader. In addition, it can import and analyze data from any scientific measurement
device.
The software supports measurements from any type of detection technology (such as
absorbance, luminescence, fluorescence, counts, counts-per-minute), with data collected from
each sample as:
l Endpoint – A single measurement value
l Kinetic – Values measured over time (data can be processed with or without time stamps)
l Spectral – Data measured at various multiple wavelengths
l Multiplex – Multiple analyte measurements from each sample
Data can be collected and processed together in any combination. For example, dual endpoint
measurements read at both 665nm and 620nm can be imported and analyzed (perhaps with
a ratio calculation). Even more exotic measurement combinations are possible with features to
process multiple endpoint, kinetic, and spectral data all together within the same analysis.
Multiple Plate Measurements
Data can be captured and processed from measurements made from a single
microplate or from across multiple plates. Data to process together can be
arranged in a single file or in separate files that can be imported and merged
together. Curve fitting can be performed with standards defined on some or all
plates, with curves computed using averages of standard measurements across
plates or from the measurements on the same plate.
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Data Import
For maximum flexibility, MyAssays Desktop provides a number of ways to get your data into
the system. These are designed to help you get started quickly and to streamline your
workflow:
l In the simplest case, copy and paste your data from any source for quick and convenient
analysis.
l The included extensive Import Script library can automatically detect and import data from
many proprietary instrument formats. Simply select the file to import; if it’s supported, the
data and any meta data (data accompanying the read such as measurement date/time,
barcode, etc.) are imported automatically. (For the latest list of currently support format,
see: https://www.myassays.com/supported-file-formats.html.)
l If your data is in an Excel document, the import wizard will guide you through the process
of importing the relevant data and meta data from the cells in your workbook. Mark the
cells to import the data (with these settings saved to your protocol).
l For other files, use the intuitive and familiar import wizard to specify how to process and
import the data from your files.
If your data is spread across multiple files, the import wizard can import your data together
for a combined analysis.
Importing Other Types of Data
In addition to first-class support for handling your measurement data, MyAssays
Desktop supports importing other types of data, including:
l Measurement meta data (data associated with your readings, such as
date/time, plate ID, operator ID, etc.)
l Worklist (e.g., containing sample IDs, dilution factors, standard concentrations)
l Plate name/IDs, barcodes, analyte names, etc.
Beyond this, it is also possible to import any data analysis parameters (including
layout, calculation settings, fit method, etc.) from data stored or produced by other
systems. In this way, your data analysis process can be fully automated or
controlled by other software.
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Data Visualization
MyAssays Desktop provides a rich modern user interface to clearly present data,
configuration, and analysis results, including:
l 2D and 3D interactive views of your measurement data
l Heatmap
l Histogram
l Interactive charts
o Overlay charts (e.g., by fit, by sample, by replicate) in any combination for convenient
visual comparison
o Zoom in/out
o Mark outliers (to exclude from analysis) directly from a chart or plate (including
outliers in endpoint, spectral, kinetic, and multiplex data)
o Specify data analysis ranges interactively (e.g., mark subsets of data to process for
kinetic or spectral analysis)
l Sample labels for data
o By name
o By color
o By graphical value bars
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Data Analysis
Beyond the online database of ready-to-use preconfigured protocol, MyAssays Desktop
provides a comprehensive data analysis engine supporting a wide range of applications.
The system provides a toolbox of preconfigured components (including over 30 transforms)
ready to bolt together in any combination. Each transform performs a single mathematical
task on measurement or calculated data. Transforms include endpoint, kinetic/spectral, and
kinetic/spectral reduction.
Transforms include:
l Blank Correction – Subtract a specified background value (or values) from all samples
l %CV – Calculate percentage coefficient of variation of replicates
l Standard Curve Fit – Calculate concentrations from a standard curve fit
l Maximum Slope – Find the maximum slope and report its values (such as slope/time to
onset/response)
For a complete list of the 30+ available transforms, see Transforms.
Transforms are linked to your sample layout. If your sample layout changes between assay
runs (or even to correct a preparation error), the analysis takes this into account. No
reconfiguration of analysis is required to work with different layouts.
The transforms parameters are completely customizable. In addition, a rich expression-based
language enables you to specify parameters and methods as mathematical operations using
an expressive familiar (Excel-like) syntax.
Results with Labels
Label your results using your own text and highlights. For example, perhaps
samples with a response or calculated concentration greater than a specified
amount should be clearly indicated as such; use labels to define your own rules
accordingly.
Validation
Add automatic checking of measurements and results. For example, check that
your samples are within an expected range by defining your own validation rules.
These are evaluated with each run of your assay, with the results clearly presented
as pass or fail in the report.
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Curve Fitting
MyAssays Desktop provides the curve-fitting tools you would expect, plus more, including:
l Quantitative tools – Weighted and unweighted 4-parameter logistic, 5-parameter logistic,
and linear regression (plus legacy methods such as cubic spline, smoothed spline, cubic
regression, point-to-point, and many more)
l Fit data to your own user-defined curve-fitting equation
l Best-Fit feature automatically compares multiple fit methods and applies the best (with
customizable scoring methods)
l Goodness of fit measures reported – R2, aR2, SE, F-statistic, P-value (and available for
further automatic scrutiny such as validation messages or best-fit scoring)
l Automatic computation of LLOQ, ULOQ, and LOD based on customizable precision and
accuracy thresholds
l View computed curve-fit equations in mathematical symbolic notation
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Reporting
For some applications, the most convenient type of report to work with is sheet-based; for
other scenarios, a page-based structure is more suitable.
l Sheet – A spreadsheet structure comprising of a workbook of worksheets of cells
l Page – Structured ties to a fixed sized printed format (e.g., A4)
For these reasons, MyAssays Desktop supports both. Easily switch between report views to
best work with your content as you need to.
Excel Objects
Exported Excel content features native Excel objects (e.g., chart content). This
means that you can edit and extend the output using the full power of Excel.
MyAssays Desktop protocols reports can be configured to include power-features
such as value bars for enhanced viewing and visualization of content.
Report Templating with Word
For page-based reporting, use the full power of Word to define a reporting
template to apply to your MyAssays Desktop output. This can include any content
you can create in Word, such as headers, footers, custom images, fonts, macros,
signature line, etc.
Analysis results are merged into your Word template for maximum usercustomizable reporting output.
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Export
MyAssays Desktop provides a wide variety of export options and settings:
l Export to XLS/XLSX/PDF/DOCX/TXT/CSV/HTML/XML.
l XSLT support is provided, enabling custom export definitions to be created for exacting
output as required by your existing system.
l Specify the export destination folder.
l Specify the naming convention of the output files, including references to imported meta
data (for example, name your output files to match a barcode imported along with your
measurement data).
l Configure a protocol to automatically export with these settings so no user intervention is
required.
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Workflow Integration
MyAssays Desktop has been designed with your workflow in mind and provides a number of
features and tools to integrate with other systems and streamline your process.
For routine data analysis, your reports can be generated without any user interaction required
at all. For example, MyAssays Desktop data analysis can be performed completely
automatically, with data (including any combination of measurement data, sample IDs,
parameters, etc.) being imported, analyzed, exported, or printed as soon as a new
measurement file is detected from your reader software.
Command Driven
MyAssays Desktop analysis provides a complete command-line interface. Launch
the analysis directly from another application or process. Specify measurements
files to process, worklists to use, output options. All data analysis parameters can
be sent from the command line.
Launcher
To facilitate working with the command line, the user-friendly Launcher utility
simplifies building your command line with a point-and-click interface.
Folder Poll
To integrate more effectively with other systems (such as instrument control
software), use the integrated Folder Poll tool to automatically launch MyAssays
Data analysis as soon as a new data file is detected.
XSLT Export
For integration with other systems, such as LIMS which might have very specific
data formatting requirements, utilize the XSLT feature to output data in the exact
format required. Example XSLT files are preinstalled to help you get started.
Advanced XML Configuration and Tooling
Inside the system, a well-design XML structure is used to define the data analysis
of every assay. Most users will not need know about its existence, but for those
specialist scenarios, advanced users can configure data analysis using a powerful
built-in XML editor complete with inline help and auto-complete features.
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Product Editions
MyAssays Desktop is available in various configurations to suit different budgets and needs:
Basic: Operates with preconfigured protocols (either those included in the installation package or as downloaded from
MyAssays.com). The Basic edition allows
tweaking limited protocol parameters (such
as fit method, sample concentrations, layout,
etc.).
Pro: Includes all of the features of the Basic
edition plus functionality for creating new
custom protocols. This edition provides full
access to the Matrix-Transform data analysis
model, offering a wealth of easy-to-use tools
for building data analysis solutions tailored
to your lab.
Pro Add-Ons:
Optional add-on functionality is available for the Pro edition:
Multiplex
Quickly and easily perform multiplex data analysis on your multiplex data:
l Calculate results from multiple analytes within a single analysis step.
l For quantitative analysis, apply the same curve-fitting method to each analyte or use the
Best-Fit option to automatically apply the most appropriate fit method for each analyte.
l Specify concentration values as a series (defining the starting concentration of each
analyte and the series) or enter specific concentrations for each analyte.
l Specify units per analyte or common units.
l Paste in or import measurement data, analyte names, starting concentration values, or
specific concentration values.
l Includes import methods to automatically extract multiplex measurement data and analyte
names from Luminex® xPONENT and Meso Scale Discovery Workbench® CSV and TXT
formats.
QC
The QC option provides a range of inter-assay analysis features to support the continuousmonitoring automatic validation that your assay remains in control:
MyAssays Desktop User Guide
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l Monitor, track, and chart observations across assay runs.
l Any numerical result can be tracked. For example, monitor raw or calculated control
values, %CVs, or curve R2 values.
l Include Levey-Jennings and CUSUM charts as part of each assay report to compare and
visualize the latest results with historical data.
l Generate QC reports on-the-fly as and when needed.
l Automatic validation can ensure that the monitored observations are within defined
acceptance parameters. Violations are clearly indicated in the report. For example, an
acceptance rule can be defined to test whether a control result of a particular assay run is
within a range of +/- 2*SD of the historical observations.
Enhanced Security Add-On
For those users working under GxP or 21CFR11, the Enhanced Security edition offers the
features necessary to achieve compliance. The software provides protected and tracked access
to system features inside a secure environment with:
l Access control – Authentication and authorization are used to permit/deny access to
MyAssays Desktop Pro functions.
l Operating-system-level security-data storage protection – Only authorized users can make
changes to MyAssays Desktop Pro data files and settings.
l Audit-trail and log-in log – All activity is stored against the logged-in user with a date/time
stamp.
l Data certification – Restrict the system to handle only certified data (eliminating the
processing of tampered data).
l Password protected and locked outputs (e.g., locked Excel outputs).
Licensing
Single, multi-seat, and site licenses are available for all editions and add-ons.
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Edition Comparison
Feature
Basic
Pro
Downloadable Protocols (from myassays.com)
Preconfigured Protocols (96/384 versions):
l ELISA
l DNA/Protein Quantification
l ATP lite protocol
l Generic DELFIA
l Generic LANCE
l Generic Fluorescence Polarization protocol
l Monochromator Spectral
l Kinetic for Ca2+
l Bacterial growth
Import Script Support
Excel Import
Worklist Import
Data Visualization (3D View/Heatmap/Kinetic
Overlay/Spectral Plot)
Versioned Analysis
Export to XLSX/DOCX/PDF
Edit Protocols
Limited
Report Customization
Limited
Custom Export (e.g., for LIS/LIMS)
Multiple Plate Support
Create New Protocols (New Protocol Wizard)
Matrix-Transform Data Analysis
Custom Validation Rules
Custom Evaluation Expressions
Best Fit
Folder Poll
PLA
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Getting Started
Installation
System Requirements
MyAssays Desktop runs successfully on the minimum system configuration listed below. If
your computer does not meet the technical requirements, you may be not able to use
MyAssays Desktop.
CPU:
Intel/AMD 2 GHz or faster processor (32bit or 64-bit)
Memory:
2GB RAM
Available disk space:
300MB
Operating systems:
Windows 10, Windows 8.x, Windows 7,
Windows Vista, Windows XP SP3
Other software:
Microsoft .NET 4.0.3 or later
Display:
1024 x 768 or higher resolution
MyAssays Desktop User Guide
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Installation Process
The latest version of MyAssays Desktop can be downloaded here.
Upon downloading the installation package:
1. Double-click on the installation package file to launch MyAssays Desktop Setup Wizard
and click Next to continue.
2. To proceed with installation on your computer, you must accept the terms of End-User
License Agreement. Please read the license agreement carefully and then check I accept
the terms of the license agreement box and press Next to continue.
If you do not agree with terms of the license agreement, click Cancel to exit the setup
wizard.
3. If you are content with the default path, press Next to continue.
To pick another destination folder, press Change… and set preferable root.
4. Check the choices you made and press Install. The process will take a few moments.
5. Click Finish to complete the installation. Check Launch MyAssays Explorer on exit to
start working with the software immediately.
6. Activate MyAssays Desktop with your product serial code and activate your license in
order to start working with the application.
MyAssays Desktop can be installed and run alongside the legacy software WorkOut.
Please refer to Installing Alongside WorkOut for further details.
MyAssays Desktop User Guide
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Launching
After installation, you will see the MyAssays Desktop Explorer icon on your desktop. To launch
the application, click on it or:
On Windows 10:
Choose Start, then scroll down to and click on the MyAssays Desktop Explorer icon.
For earlier versions of Windows:
l Windows 8.1 or Windows 8: Swipe up or choose the arrow at the bottom of the Start
screen to see a list of all your applications. You might need to scroll left or right to see the
MyAssays Desktop Explorer.
l Window 7: Choose Start > All Programs to see a list of all your applications and click
the MyAssays Desktop Explorer icon.
To access MyAssays Desktop Explorer more conveniently, you can pin it to your Start
menu or taskbar. Right-click on the desktop icon and select Pin to Start or Pin to
Taskbar.
.
MyAssays Desktop User Guide
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Product Licensing
To use MyAssays Desktop, you must provide and activate a valid license.
You can obtain a trial, temporary, or permanent license from MyAssays Ltd.
A serial code represents your license or licenses (for multi-seat site licenses) to use the
software. When you first launch the software, you must enter a valid serial code and activate
your license.
A single license can be transferred between systems but can only be active on one
system at a time.
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License Activation
On launching MyAssays Desktop, if there is no valid license already installed, then the
Licensing Activation Wizard will appear.
An administrator account is required to activate (or deactivate) a license.
If you have a serial code, select the I have a serial code to activate button and press Next.
If you do not yet have a serial code, then you can obtain a trial serial code from
https://www.myassays.com/download-and-install-myassays-desktop.html by
clicking the Claim Key option.
Paste in the serial code along with your email address into the Licensing Activation Wizard
and press Next.
Your email address is required for administrating your license. Access to this email
account will be required to transfer your license to another system in the future.
Online Activation
If your PC is connected to internet:
1. Check the Use internet connection for activation checkbox and press Continue to
proceed.
2. Fill in your personal details and press Activate.
3. When prompted to grant User Account Control permission to access the licensing
settings, confirm by selecting Yes.
4. If the license activation is accessed, a confirmation window appears.
5. Press Close to exit the License Activation Wizard and start the application.
Offline Activation
If your PC is not connected to internet, it is still possible to activate MyAssays Desktop using a
separate internet-connected device. In this case:
MyAssays Desktop User Guide
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1. Uncheck Use internet connection for activation checkbox and press Continue to
proceed.
2. A QR code will appear; if you have a network-connected device (such as a smartphone
or tablet) capable of reading this, then scan the QR code and follow the instructions.
Alternatively, copy and paste the provided link and open it on a system with internet
connection (perhaps by transferring via USB memory device).
3. On successful activation of your serial code from your internet-connected device, a
license key text string will be generated (with a copy sent to the provided email
address).
4. Transfer this text string to your PC where MyAssays Desktop is installed and paste in the
License Activation Wizard under the license key setting.
5. When prompted grant User Account Control permission to access the licensing
settings and confirm by selecting Yes.
6. If the license activation is accessed, a confirmation window appears.
7. Press Close to exit License Activation Wizard and start the application.
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Licensed Features
There are various editions of MyAssays Desktop as well as optional add-ons. The features
available to you will depend on your license. To see which features are included in your
license:
1. Launch MyAssays Desktop Explorer.
2. Select the File tab and select the Information option:
The availability of licensed features is marked with the following icons:
for active features.
for features not supported by your current license.
To learn more about additional features, which are not included in your existing MyAssays
Desktop license, click on the Upgrade License button.
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License Deactivation
If you need to transfer an activated license to another PC, it must first be deactivated from the
PC where it is in use.
To deactivate MyAssays Desktop on your PC, launch the Deactivation Wizard as follows:
1. Launch MyAssays Desktop Explorer.
2. Set File tab and select Information section.
3. Click on the Deactivate License button.
Follow the steps of the License Deactivation Wizard as described below to deactivate the
license:
Online Deactivation
If your PC is connected to internet:
1. Keep the Use internet connection for deactivation checkbox checked and press
Continue to proceed with deactivation.
2. Check the checkbox to confirm deactivation and press Deactivate.
3. When prompted to grant User Account Control permission to access the licensing
settings, confirm by selecting Yes.
4. On successful deactivation, a copy of the license serial code will be sent to the email
address it is associated with, ready for activation on another system.
5. Press Finish to close the License Deactivation Wizard.
Offline Deactivation
If your PC is not connected to internet, it is still possible to deactivate MyAssays Desktop using
a separate internet-connected device. In this case:
1. Uncheck the Use internet connection for deactivation checkbox and press
Continue to proceed with deactivation.
2. Check the checkbox to confirm deactivation and press Deactivate.
3. When prompted to grant User Account Control permission to access the licensing
settings, confirm by selecting Yes.
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4. A QR code will appear; if you have a network-connected device (such as a smartphone
or tablet) capable of reading this, then scan the QR code and follow the instructions.
Alternatively, copy and paste the provided link and open it on a system with internet
connection (perhaps by transferring via USB memory device).
5. On successful deactivation of your serial code, a confirmation message will appear in
the browser and a copy of the license serial code will be sent to the email address it is
associated with, ready for activation on another system.
6. Confirm that the email has been received in the License Deactivation Wizard and
press Finish.
After successful deactivation, the application will be closed and will not be usable
until it is reactivated. The deactivated license will be immediately available for
activation on another system.
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Activate New License
If you have been provided with a new serial code to replace an existing license (perhaps as the
result of a purchased upgrade or support extension), then it will be necessary to activate your
new license to replace the existing one.
1. Launch MyAssays Desktop Explorer.
2. Select the File tab and select the Information option.
3. Select Activate New License.
4. Press Yes to confirm the removal of the existing license.
5. When prompted to grant User Account Control permission to access the licensing
settings, confirm by selecting Yes.
6. Follow the steps of the License Activation Wizard as described in the License
Activation section to activate new license.
7. On completion of the wizard, any new license updates will be applied to your installation
and any new features or support extensions will be available.
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Update Existing License
If new features have been added to your existing license (perhaps as the result of a purchased
upgrade or support extension), then it will be necessary to update your activated license with
the latest information.
1. Launch MyAssays Desktop Explorer.
2. Select the File tab and select the Information option.
3. Select Update Existing License.
4. If your PC is connected to the internet, then select Update my license online in one
click. Otherwise, select Get a unique link to my activation code and activate using
a separate device that is online (as described in License Activation).
5. When prompted to grant User Account Control permission to access the licensing
settings, confirm by selecting Yes.
6. On completion of the wizard, any new license updates will be applied to your installation
and any new features or support extensions will be available.
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Quick Start
MyAssays Desktop – The Basics
This section introduces the basics of the MyAssays Desktop system by providing a summary
of the key features and functions and where to find them.
This section is intended to be short to help equip you with the basics so that you can start to
be productive with the software quickly and feel comfortable to explore further functions
yourself.
Firstly, MyAssays Desktop provides two key applications to help organise and work with your
data:
l MyAssays Desktop Explorer (MADE) – For arranging, locating, discovering, and launching
your assay data
l MyAssays Analysis (MAA) – For performing the assay data analysis
The following subsections provide orientation details for working with both of these
applications.
l Introducing MyAssays Desktop Explorer
l Introducing MyAssays Analysis
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Introducing MyAssays Desktop Explorer
Orientation
The Welcome screen is displayed on launching MADE. Its main area provides short-cut links
to:
l Previously used protocol and results files
l Online resources including video tutorials
l The protocols and results folders
l The New Protocol wizard (Pro versions only)
Along the left-hand side of the MADE window, you will see tabbed sections to conveniently
access the folders that contain the various types of assay data.
Along the top of the MADE window, you will see the Ribbon providing various commands and
functions for navigating and managing your data.
Here you can find the Search MyAssays.com control, which provides a convenient function
to search the online database of preconfigured protocols. Simply enter any appropriate search
term such as assay or kit name, product or kit manufacturer name to search for available
downloadable content.
Assay Data
If you’ve just installed MyAssays Desktop, you will find some preinstalled assay protocols and
layouts under their respective tabs. The results and reports tabs will be empty until you’ve ran
some analyses and created some reports.
As you run analyses and create various content, MyAssays Desktop will automatically store
content in the associated folders.
Where is My Data?
It is worth noting that all of your MyAssays data is stored under a single parent folder. This
makes backing up your data a simple process of copying all of the data in this folder. The
default location of MyAssays data is the subfolder located under your Windows Documents
folder.
You may prefer to store your data in a location different to this default location. For example,
you may want to:
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l Use a common location shared with your colleagues
l Store your data in another location
To access this location setting, select File | Data, the Assay Data Folder Root section reports
the current setting. Use the Select Folder Root option to change this as required. This setting
is stored with your Windows login account (so will be remembered the next time you log in).
You can of course access all of your data outside of MADE and in Windows Explorer, simply
navigate to assay data folder root directory.
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Introducing MyAssays Analysis
Orientation
The MAA application is displayed when launching an assay protocol or opening an existing
assay results file.
The Overview tab is typically selected when launching MAA. This tab displays the launched
assay name and any description. It might also include a link to online content (such as kit
manufacturer’s product insert).
Along the left-hand side of the MAA window, you will see various tabbed sections for
providing and reviewing the data and results for the analysis. All assays will contain the tabs:
l Overview
l Measurements – From here you can provide and review your measurements (and any
meta-data). Various subtabs are provided for viewing your data in different formats.
l Results – After performing calculations, the results of the analysis will appear in this tab.
This will include the report. If the report is arranged as multiple worksheets, you will see
worksheet tabs at the bottom of this area for navigating between worksheets. Also,
depending on the analysis performed, you might see various subtabs appearing to the
right of a Report subtab; these provide interactive controls for working with your data in
various ways (such as flagging outliers from a Standard Curve Fit).
In addition to the Overview, Measurements and Results tabs, your assay protocol might be
configured to include further tabs such as:
l Microplate – For defining, reviewing, and managing the sample layout.
l Sample IDs – For providing ID values for each unknown sample.
l Dilution Factors – for providing dilution factors for each unknown sample. These will be
applied to the concentration value computed from the curve.
There might also be further tabs displayed for providing data specific to the assay.
Assay Protocol and Assay Results
When an assay protocol is launched, MAA operates in protocol mode. In protocol mode you
can provide the data for your analyses and optionally save the settings to the protocol file.
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Provide your analysis assay parameters along with your measurement data (or use the
provided example data) and then press the Calculate button to create the first set of results.
For all assay protocols, typical example data is provided to help you familiarise
yourself with the analysis when you don’t have any data to hand.
Pressing the Calculate button:
l performs the calculations
l generates the report
l and performs any configured export operations
For the first set of calculations, some additional steps are automatically performed as MAA
transitions from protocol to results mode:
l The results appear in the Results tab.
l An assay results file is created. This contains a copy of the assay protocol that was
launched and all of the data provided (measurements and analysis parameters).
l The assay results file is automatically named and saved on the first calculation.
The file naming method can be changed under File | Save | Results Naming.
Results
The calculated results can be found under the Results tab. If the analysis generates charts,
then these will also appear in subtabs under Results.
When there are multiple subtabs, the report can be found under the Report subtab. If the
report is arranged into multiple worksheets (in Sheet View), then the sheet can be select
using the sheet names appearing at the bottom of the Report subtab.
Page View or Sheet View
The report can be displayed as Sheet View or Page View; these settings are available
through the Ribbon’s Report tab.
When getting started, we suggest using the Sheet View so that your outputs are not confined
by the limitations of a printed page. Use the Page View only if and when the layout of the
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printed page is important (For page-based reporting, you can define a reporting template to
apply to your MyAssays Desktop output).
Save the Changes to the Protocol?
Before the first calculation is performed (protocol mode) any interactions performed apply to
the launched protocol. If you perform the Save operation at this point, then the changes will
be saved, updating the original protocol file. Also, on the first set of calculations the settings
are automatically saved, updating the protocol.
This workflow might be useful if you want your changes to apply to future runs of the
protocol (so you don’t need to repeat the configuration again next time). Alternatively, you
might want the changes to only apply to this particular analysis.
For example, perhaps you are running a routine assay but have used a slightly different
sample layout than you usually use. You might want to apply this change to the current run
only, or you may decide to use this layout in future runs too.
To configure the behaviour that best suits you select File | Save and set the Save changes
to the protocol option as you require. With this option set, any changes you make to the
launched protocol are saved to the protocol. The changes are also save to the protocol on the
first calculation (as the system transitions from protocol to results mode). Any further changes
in results mode will then only apply to the results.
This Save changes to the protocol setting is stored across sessions so it will apply
to any other protocols you launch in the future.
Further Save Options
Under the File | Save area, you will see further self-explanatory save options:
l Save
l Save Results As
l Save Protocol As
Note that the Save Protocol As option can be used when viewing results to extract all of the
current configuration settings to a new protocol file. This is useful to package up all of your
recent changes made to a results file to a new protocol file.
Results Naming
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Before any calculations are performed (protocol mode), the Results Naming option is
available to enable you to specify how the created assay results file is named and stored. For
example, you could name the results file using meta-data imported from the measurements
data file and/or customize the subfolder where it is stored.
Save the changes to the protocol so any future runs of the protocol will follow the naming
convention you have specified.
Calculate
Press the Calculate button when you are ready to calculate the results. In case this is not
obvious, if you make any changes to the data or parameters you will be reminded to perform
the calculations when you are ready.
MAA does not perform calculations automatically on every setting change because
this could impact on your experience of working with the software. With larger data
sets (such as multiplex, kinetic or multiple plate assays) it might take a few seconds
to perform the analysis.
Result Versions
Each time the Calculate button is pressed a new version of results is created. You can find the
version controls including the full version history of the results in the Home | Versions
Ribbon group.
To view a different version, use the Current Version selector. Changing a version will update
the whole of the MAA application to reflect the newly selected version; all data views reflect
the data and results for the selected version.
If you have made changes but not yet calculated the results of these changes, then the
Current Version selector will display the new version number with the text (pending)
following it. This is to indicate that the new results are pending calculation. Press the
Calculate to create the new results version.
If you change version when results are pending calculation, then those changes will be lost (a
warning message will prompt for confirmation before this action is completed).
You cannot make changes to an earlier previously calculated version; any changes
made will apply to a new result versions pending calculation.
In addition, you can remove unwanted versions using:
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l Purge – Removes all versions except the one currently selected.
l Rollback – Removes all versions that follow the selected version.
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Video Tutorials
Online videos are provided to help you get started and walk through features in detail:
Getting Started
l http://www.myassays.com/myassays-desktop-fundamentals.video
l http://www.myassays.com/download-and-use-protocols-from-myassays.video
Importing Data
l http://www.myassays.com/import-measurement-data-from-excel-files-with-myassaysdesktop.video
l http://www.myassays.com/import-measurement-data-from-multiple-excel-files-withmyassays-desktop.video
ELISA
l http://www.myassays.com/elisa-data-analysis-with-myassays-desktop.video
l http://www.myassays.com/multiple-plate-elisa-with-myassays-desktop.video
Worklists
l https://www.myassays.com/myassays-desktop-sample-ids-and-worklists.video
Export
l https://www.myassays.com/myassays-desktop-custom-export-for-lims.video
Multiplex
l https://www.myassays.com/myassays-desktop-introducing-the-multiplex-protocol.video
l https://www.myassays.com/myassays-desktop-automatic-launching-of-multiplexanalysis.video
Folder Poll
l https://www.myassays.com/folder-poll-with-myassays-desktop.video
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Working with MyAssays Desktop
Key Concepts
Throughout MyAssays Desktop, various concepts and terms are used to communicate and
reference the different types of data related to assay data analysis. This section introduces
these fundamental concepts.
Measurements
The measurements are the data points acquired from your scientific instrumentation (e.g.,
microplate reader).
The software supports measurements from any type of detection technology (such as
absorbance, luminescence, fluorescence, counts, counts-per-minute), with data collected from
each sample as:
l Endpoint – A single measurement value
l Kinetic – Values measured over time (data can be processed with or without time stamps)
l Spectral – Data measured at various multiple wavelengths
l Multiplex – Multiple analyte measurements from each sample
Measurement data is typically accompanied with other types of data; examples include
date/time of measurements, plate ID, and user ID. We refer to this data as meta data. The
software can import and report this alongside your measurement data.
Import
The import process gets your measurement and any meta data into the software. The
software supports a variety of methods including:
l Copy and paste
l Excel selection import
l Automated import for supported third-party proprietary formats
Protocol
An assay protocol defines all of the parameters necessary for performing the data analysis
required for any assay. This includes:
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l Expected measurement data (e.g., endpoint, kinetic, spectral, multiplex, etc.)
l Layout – How samples are arranged
l Calculations
l Report content and configurations
Also, the protocol can define:
l Import settings (exactly how and what data to import)
l Export settings (how to export the data, e.g., to Excel/PDF or a custom file such as CSV/TXT)
l Quality control settings (intra and inter-assay calculations and settings)
With MyAssays Desktop, the settings for a particular assay protocol are stored in an assay
protocol file. These files use the .assay-protocol file extension.
An assay protocol file is a self-contained file with no dependencies on any database,
making it easy to transfer protocols between systems and to share protocols with
colleagues.
Results
Each time an assay protocol is launched and results are calculated, an assay results file is
automatically created. The assay results file contains the results of the analysis including the
measurements and meta data and any data specific to the run. This could include:
l Sample IDs
l User-entered notes
l Calculation log
l Any changes to the configuration such as:
o New sample layout
o Calculation settings
o Any flagged data (i.e., marked as outliers)
Assay result files use the .assay-results file extension.
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An assay results file also contains its own copy of the assay protocol. This is
important so that any changes to the original assay protocol file will not impact the
results already calculated. This structure also simplifies sharing data and transferring
results between systems.
In addition, an assay results file can contain the whole version history of changes made to the
analyses. Each time results are recalculated, a new set of results is created and stored within
the same results file. The full-version history is available so you can go back to earlier versions
of the results.
Reports
The report is the primary output of interest of the data analysis. A report is generated each
time the assay results are calculated.
Typically, the report will contain a table listing the measurement data and results for each
sample. The report might also contain charts and other content. MyAssays Desktop Pro
provides a wealth of features and functions for tailoring the report content to your exact
needs.
Report Views
Depending on your data or preferences there are two types of report, each with their own
benefits:
l Sheet view
l Page view
Exports
MyAssays Desktop can export the report content to a variety of formats, including
Excel/Word/PDF. In addition, content can be easily output to popular text-based formats such
as CSV using preconfigured export methods. Beyond this, XSLT support is provided to enable
you to export your content in any format to exactly match you output requirements.
Sample Layout
The sample layout (or simply layout) defines how the samples are arranged on the container
or containers from where they were or will be measured.
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Here we use the general term “container” because the samples could be measured in
a microplate or a rack of vials; in fact, any vessel for your samples is supported.
An assay will contain different types of samples such as:
l Unknown – Samples to quantify or qualify
l Standard – Samples with a known quantity used for preparing a calibration or standard
curve
l Control – Samples used for control purposes such as inter-assay or inter-assay quality
control
l Blank – A sample without any expected response, used to remove background noise
Other sample types are possible; you can create and name your own sample types as you
require.
With MyAssays Desktop, we use a color to indicate sample type:
Standard
Blank
Control
Unknown
Typically assay samples will be replicated. To identify replication, each sample is given a
number. Thus a sample type and number uniquely identifies a sample. All positions on a
layout with the same type and number are replicates of the same sample.
For example, a layout with Standards, Control, Blank and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
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The use of a sample layout introduces a convenient layer of abstraction over the
measurement data. For example, being able to refer to, for example, Control1 to reference all
Control1 replicates is much simpler than having to remember or decode the measurement
data. Also, MyAssays Desktop calculations are based around the sample layout, therefore you
can easily change the sample layout (perhaps because you need to use a different positioning
of your samples on a different run of your assay) without needing to make any changes to
your calculation settings. If you have ever had to change an Excel spreadsheet because of a
change in layout, then you will appreciate that this abstraction is a valuable time saver.
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Getting Around
MyAssays Desktop provides two key applications to help organize and work with your data:
l MyAssays Desktop Explorer – For arranging, locating, discovering, and launching your
assay data
l MyAssays Analysis – For performing the assay data analysis
MyAssays Desktop Explorer (MADE)
MADE is the application launched when you click the MyAssays Desktop Explorer icon
installed on your desktop. This application is the starting point for all MyAssays Desktop
functionality. From here you can:
l Search and download preconfigured assays from MyAssays.com
l Access useful videos and training guides
l Access technical support
l Manage your product license
l Access your assay data
l Create new assay protocols (requires Pro version)
MyAssays Desktop Analysis (MAA)
If you launch a protocol or open a results file, MAA opens. MAA provides the user interface for
working with your assay data analysis. From here you can access all of the various options,
including:
l Import/export data
l Provide assay parameters
l Enter sample IDs
l Generate report
l Calculate new results
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MyAssays Desktop Explorer
MyAssays Desktop Explorer is the first application of MyAssays Desktop you interact with.
With MADE, you can:
l Find, download, and launch assay protocols from MyAssays.com database
l View existing assay layouts, results, and reports
l Run assay protocols and open results
l Create or request an assay protocol
Assay protocols and results can also be launched directly from Windows Explorer
MADE Ribbon
Ribbon extends across the top of the main area of the MADE.
Ribbon Tabs:
Home
This tab consists of several groups for operating with data files inside MADE directly.
l Navigation includes three main options for moving around tabs, folders, and subfolders
inside the application.
As you continue working on your data analysis, you may prefer to divide and arrange your
data in folders by its measurements or features. Therefore, the number of folders and
subfolders in tabs will grow. You may use the Back, Forward, and Up navigation
commands on Ribbon or Quick Access Toolbar to change your current location quickly.
l Clipboard provides better management of your stored data. You may prefer to rearrange
the way your files or folders are stored by using Paste, Cut, and Copy commands. This
way you can simply move around data files between various storage folders.
Clipboard data is automatically deleted when the computer shuts down or restarts,
so remember to save important information.
l File consists of two commands: New, which launches New Protocol Wizard, and Open,
which launches data from MADE’s tabs.
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l Search MyAssays.com can be of the maximum use for finding and downloading specific
preconfigured assays from the MyAssays online database. This tab consists of Search
textbox and Search button.
Tools
This tab embodies Utilities you may find useful.
l Analysis launcher is a tool for advanced launching of the MAA.
l Folder Poll Manager launches MyAssays Folder Poll Manager.
Support
This tab provides connection between MyAssays and the end users.
Assistance group has a Request Protocol command, which you may find useful if the assay
template you are looking for is not yet in the MyAssays database.
It also features a backstage view for additional use of application scenarios and configuration.
Backstage Area:
File Tab
File Tab gives you access to MADE's important tasks.
l Information contains details about your MyAssays Desktop personal use. This includes
product version, current license details, and features.
l
You may want to change some features of your MyAssays Desktop (i.e., activate,
deactivate, or update license). The Product Licensing describes further details for
these options.
l View defines whether the Measurements Tab is displayed on the Folder Side Bar and the
Launch Assay Protocol with Options setting is displayed on preview pane in the Protocols
tab.
l Data displays the storage path for persisting your assay data files and Welcome Screen's
Recent feature options.
l Drivers
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l Exit closes the application.
Quick Access Toolbar
Quick Access Toolbar contains shortcuts to commands and functions to make your work on
MyAssays Desktop Explorer easier and personalized.
You can Customize the Work Area to fully adjust it to your needs.
Application Button
Click on the logo in the upper left corner of the MADE to: l Restore – Return window size to the previous one
l Move – Change windows on-screen’ locations, if they are not full screen
l Size – Widen or narrow the window
l Minimize – Put the window in taskbar
l Maximize – Make the window full screen
l Exit – Close the program
Navigation Shortcuts
The other default commands of Quick Access Toolbar are the ones from Navigation group,
namely Back , Forward , and Up , which are useful for switching between the tabs you
currently work with quickly.
Advanced Options
You can set the Quick Access Toolbar position in this drop-down menu. Click on Show Quick
Access Toolbar Below/Above the Ribbon (depending on its current location), Minimize
the Ribbon , and change the Quick Access Toolbars view by adding or excluding some
options in More Commands pop-up menu.
Folder Side Bar
MADE provides a hierarchical view of the organized data and presents its preview if it is
available. MyAssays Desktop stores different types of data in separate subdirectories in the
MyAssays Data Folder Root. These tabs point to actual Windows folders.
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In MADE, you can access information from every subdirectory easily in the appropriate left
side bar tab.
MyAssays Desktop arranges your data automatically in correspondent folders so that
you can easily manage, share, and back up your assets. You may prefer to customize
how results files are named and where your data is stored.
Folder Side Bar Content
Tab
Contains Files
Search Results
Assay protocol titles that match search query
Protocols
Assay protocol files with .assay-protocol extension
Measurements
Measurement data files in supported file formats (*.TXT, *.CSV,
*.XML, *.DAT, *.XLS, *.XLSX, and others)
Results
Assay results files with .assay-results extension
Layouts
Layout files with .assay-samplelayout extension
Reports
Report files with supported report extension (*.DOC, *PPT, *.XLS,
*DOCX, *XLSX, *PPTX, *.MHT, *.HTM, *.PDF)
Selecting a tab displays its content in the main window.
File and Preview Panes
The File and Preview panes are populated with the content of the selected tab.
File Pane
A list of available data files displays on the left side of the active tab. It is a visualization of the
folder that persists data, that was used to launch, or is a result of performing a particular assay
analysis.
In the File Pane, files can be selected using normal Windows procedures. Right-click to display
the default Windows Explorer context-sensitive pop-up menu. Relevant MADE Ribbon options
are available when a single file is selected.
Sort and View
The default view of content is Details. To change it, right-click on active tab’s empty space >
View > select preferable view from the drop-down list.
Files are sorted by name by default. To change the file sorting order:
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l Right-click on the empty space of the active tab > Arrange Icons By > choose preferable
option form the drop-down list.
OR
l If File List is displayed in default view, click on the column headings Name, Size, and Date
Modified.
Subfolders
Tabs may contain subfolders (subdirectories). Creating subfolders in tabs comes in handy
when working with big data sets or with multiple versions of analyses. All subfolders will be of
the same type as their primary parent. To create a new subfolder, right-click in the File Pane
and select New > Folder.
The File Pane may be navigated using the relevant buttons on the MADE Ribbon.
Preview Pane
Select a single file from the list to see its preview and available actions on the right side of the
active window.
The contents of the preview pane depend on the type of file selected:
Folder Tab
Preview Pane Contents
Search Results
A summary of assay protocols, including the name, the instrument
settings, the position used and layout, and any configured transformations
Protocols
Measurements
Pre-canned and custom raw-data files
Two assets:
Results
l Overview with the assay protocol summary
l Latest Report with the latest report of the results
Layouts
A microplate control showing each plate layout in the layout
Reports
The preview of the PDF reports
Welcome Screen
This is the screen that displays as you launch MADE. It contains some useful options for a
proficient start. Get acquainted with MyAssays Desktop Explorer’s basic functions and begin
your work at full speed.
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The left pane of Welcome screen consists of:
l Start – Shortcuts to basic data analysis options, particularly New Protocol..., Launch
Protocol..., and Open Results...
l Recent. – A specified number of assay protocols and results you have worked with last
(the particular title will be launched in MAA upon selection)
You can customize a number of entries in the Recent list or clear the file history
in Data options.
The right pane comprises of two subpanes:
l Upper subpane Discover MyAssays Desktop displays links to a detailed review of
MyAssays tools and the desktop application specifically. Visit MyAssays Home Page or
What’s New in MyAssays Desktop? and get to know latest information about desktop
version.
l Lower subpane contains:
l Product Videos – Links to video materials that will help you familiarize yourself with
MyAssays Desktop and its components
l Support – A link to the MyAssays Support contact form and the Request an Assay
Protocol dialog box
Recent Used Files Feature
The number of files displayed under the Recent subpane of the Welcome Screen may vary
from 0 to 20.
To change the number of Recent files, type it in the corresponding box or use the arrows to
adjust it.
Press the Clear Recently Used File list button if you want to erase the history.
Search Results Tab
This tab displays on the Folder Side Bar after you submit a Search MyAssays.Com.
It displays a File List and Preview of the assay protocols that match search query.
When this tab is active, multiple search results options are available.
Search MyAssays.com
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How to: Find an Assay Protocol from MyAssays.com Knowledge Base
First, in MADE, enter an assay title or keyword (i.e., Cortisol, Human, ELISA):
l In the MADE Menu Bar, Search MyAssays.com panel
OR
l Use a keyboard shortcut (CTRL + E), which automatically redirects you to a
search textbox
Next, press the Search button or Enter on keyboard.
When you have performed a search, the Search Results tab is displayed on Folder Side Bar.
Using the Search Results
Upon performing a search, you will see the list of assay protocols that contain a keyword you
have entered.
To specify the search results and ensure finding the protocol you’re looking for, each protocol
contains a detailed summary. To see further information on a particular position, single-click
on its title. Some of the assays contain Kit Booklet from the manufacturer. To read it, click on
View Assay PDF link under the assay summary.
For every assay protocol in the list, the following actions are available on the Preview pane:
l Download and Launch – Persists a single assay protocol to your Protocols tab and
automatically launches it in MAA.
You can also double-click on a file from the list to download it to your PC and
launch it in MAA automatically.
l Download – Stores a single or multiple assay protocols to your Protocols tab.
If you select multiple items from the list, only this option is available and
displayed. To select multiple protocols from the search results, use the (Ctrl+Shift+Arrow Keys) keyboard shortcut and Ctrl+A to select all search
results.
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l Launch Assay Protocol – Appears if you have downloaded a particular assay protocol
previously. The above actions are then not displayed. To enable re-downloading, click on
the Click here hyperlink in the message below Visit on MyAssays.Com.
l Visit on MyAssays.Com – Directs you to the online MyAssays.Com webpage of the
selected assay protocol.
No Results Found
If the assay you are looking for is not yet in the MyAssays database, an Information dialog
box appears.
Click Yes to proceed to a Request Protocol form.
Search + Download from MyAssays.com
Download and use preconfigured assay protocols from the MyAssays.com knowledge base on
your PC.
Submit a Search and explore search results options of the constantly evolving protocols
database.
Protocols Tab
The Protocols Tab displays a File List and Preview of the assay protocols that are persisted to
your PC.
Only data files with an .assay-protocol or .apr extension are visible through this
folder.
Upon selecting a particular assay title from the list, you will see its summary on the Preview
pane and, if it is available, a detailed Booklet Kit from the kit manufacturer.
In the Protocols Tab, you can:
How to: Create New Assay Protocol
In the MADE Welcome Screen, click on New Protocol....
OR
In the Folder Side Bar, select the Protocols tab and click on the New button on the MADE
Ribbon.
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When this option is selected, a New Protocol Wizard will be displayed.
How to: Launch Assay Protocol File
Navigate to the Protocols tab, select a single assay result file to open, and do one
of the following:
l double-click on its title
l right-click on the title and choose Run with MyAssays Desktop Analysis
l click on the Launch Assay Protocol button in the lower right corner of the
Preview pane.
By doing this, you will launch the file in MAA.
You can also launch an assay protocol directly from Search Results.
Launch Assay Protocol with Options
Launch an assay protocol with command-line options for more control over the analysis. This
feature will open the Launcher tool with the selected protocol.
If you press the Launch Assay Protocol with Options button, the dialog window with a
visual representation of supported command-line options will appear.
How to: Launch Assay Protocol with Command Line Options
1. Launch MADE.
2. Select the File tab of MADE Ribbon.
3. Select the View screen.
4. Check the Show Launch Assay Protocol with Options button checkbox.
The Show Launch Assay Protocol with Options button state
(checked/unchecked) persists between sessions.
If it is checked, a corresponding option appears on the preview pane of the Protocols tab upon
protocol file selection.
How to: Request Assay Protocol
Launch MADE and do one of the following:
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l Click on the Request an Assay protocol on a Support subpane of the
Welcome Screen.
OR
l On the MADE Ribbon, select the Support tab and press the Request an Assay
protocol button.
When this option is selected, a Request Protocol dialog box will appear. Fill in the fields to
specify what kind of an assay you want to be included in the MyAssays knowledge base.
Please mention any information that can be of use in Other Significant field.
You can and are welcome to attach any applicable files, such as Example Raw Data, Kit Insert
PDF, and Other files (in zipped files).
After you have provided details of an assay, press the Submit Request button to send your
requirements to MyAssays Support.
Measurements Tab
How to: View the Measurements Tab
1. Launch MADE.
2. Select the File tab of the MADE Ribbon.
3. Select the View screen.
4. Check the View Measurements checkbox.
The View Measurements state (checked/unchecked) persists between sessions.
If it is checked, an additional tab is displayed on the Folder Side Bar.
The measurements tab contains a default Examples folder with a 12x8 Endpoint.txt file. For
each assay, a text file containing your measurement settings data can be uploaded and
processed for analysis. To upload a file, paste it in the Measurements folder in your
MyAssays Folder Root.
The New protocol wizard can automatically identify and import the relevant readings from a
file saved in many supported file formats, including TXT, CSV, or other proprietary formats.
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Visit MyAssays Supported File Formats (http://www.myassays.com/supported-fileformats.html) to see list of supported measurements data formats.
Results Tab
The Results Tab displays a File List and Preview of the results of previously run data analyses.
Select a single Assay Results file from the File List to see the Preview of the report.
Results files generate when you have launched a particular assay protocol and performed a
data analysis. It is empty on your first time launching MADE and will fill as you work with the
application. By default, these files are named after a protocol, but you can change the name
(i.e., after incoming measurements, current date, etc.).
Only assay results data files with an .assay-results or .ars extension are visible
through this folder.
When you select a title, two information insets appear on the Preview pane. The Overview
shows an assay summary and may contain a link to the Booklet Kit from manufacturer. The
Latest Report contains the report of the selected Assay Results file.
To enlarge the Preview view, hold down Ctrl button and push the mouse scroll wheel
upward.
In the Results Tab, you can Open Results in MAA.
Open Existing Results
How to: Launch Assay Results File
Navigate to the Results tab, select a single assay result file to open, and do one of
the following:
l double-click on its title
l check the Open icon on the MADE Ribbons File group
l check the Open icon in Latest Report inset of the Preview pane
By doing this you will launch the file in MAA.
Layouts Tab
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The Layouts tab displays a File List and Preview of the assay layouts persisted to your PC. Vice
versa, only files persisted to this tab may be used in running assays by MyAssays Desktop
Analysis. Select a single Assay Layout file to view it on the Preview pane.
Only data files with an .assay-samplelayout or .mlo extension are visible through this
folder.
Assays in MyAssays Desktop Analysis are initially set up to use a default layout for your
samples. You will most likely prefer to use your own sample layouts in your data analyses.
Use the Layout Editor to edit existing or create new layouts.
Reports Tab
All assay result data exported into a report file is stored to this folder. Double-click on a title to
open it in a relevant application.
Customize the Work Area
You can configure Work Area to fully match your preferences and enhance your experience.
Learning about its potential may be useful for taking full advantage of the features, tools, and
options of the application.
Customizing the Quick Access Toolbar
The Quick Access Toolbar displays tools that you have bookmarked. For making use of its full
potential, you may prefer to rearrange and manage these tools.
Location
You may prefer the Quick Access Toolbar to be displayed above or below the Ribbon. Rightclick on any spot of the Ribbon and hit on the Show Quick Access Toolbar Below the
Ribbon command. If the toolbar is already below, you can configure it to be vice versa.
Adding or Excluding Tools
To add a command to the Quick Access Toolbar, choose the one you want from the Home,
Tools, or Support tab and right-click on it. In the drop-down menu that will appear, press the
Add to Quick Access Toolbar command. Upon doing that, the command will be displayed
in the Quick Access Toolbar.
To exclude a command from the Quick Access Toolbar, right-click on it and click on Remove
from Quick Access Toolbar.
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Advanced Customizing
You may consider customizing the Quick Access Toolbar to fully match your preferences. To
call up a Customize Quick Access Toolbar options menu, do one of the following:
• Right-click anywhere on the Ribbon and click on Customize Quick Access Toolbar on the
context menu.
• Click on the Advanced option on the Quick Access Toolbar and click on the More
Commands command.
Upon doing this, a dialog box will appear. Select tools you prefer and add or remove them
from the list.
You can add or remove commands from a list or from a specific tab. You can also determine
their on-screen order by pressing the arrows on the right side of the Quick Access Toolbar
list.
When you have finished customizing toolbar, press the OK button to save changes or Cancel
to undo them.
To return the toolbar to its default configuration, click on Reset in the Customize Quick
Access Toolbar dialog box.
Customizing the Ribbon
The Ribbon is designed to make finding and working with commands convenient and easy.
You may consider the capability to personalize and customize the Ribbon in the way you want
useful. With this feature, you can change the default Ribbon and create custom tabs and
groups with your preferred commands.
To customize the Ribbon, right-click anywhere on the Ribbon and then choose the Customize
Quick Access Toolbar context menu to bring out the dialog box. Select the Customize
Ribbon option and personalize your Ribbon view for your convenience.
Minimize Ribbon
This option may be useful to expand your on-screen view of the application. If the Ribbon is
minimized, tabs appear on click and hide when you click on any other spot of the screen. To
do this, right-click on any spot on the Ribbon and check Minimize Ribbon in the drop-down
list. To undo minimizing, repeat these actions and uncheck the command.
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Layout Editor
Layout Editor
Overview
Most assays allow you to specify your own layout for processing and distinguishing various
types of measurement data.
You can freely position your samples and change the number of sample groups providing the
requirements of the assay are met. Certain assays allow you to provide measurement data ran
across multiple plates. Create new or modify existing layouts after analysis and easily
recalculate results to correct for preparation errors.
Using the Layout Editor
The Layout Editor is displayed when you press the Create button to create a new layout or
Edit to edit an existing layout on the Layout Selection pane of MAA Microplate tab. You
can also create a new layout as a step of the New Protocol Wizard.
Editor Flexibility
The actual flexibility available in your custom layout depends on an assay’s requirements.
Most assays require a certain number of positions of a particular type to be defined (e.g.,
Blank or Standard groups). In most cases, however, you may setup the layout as you prefer
providing your layout meets an assay’s requirements.
Once your new layout is created, you can select it to be applied to future calculations of an
assay. By making calculations or saving settings, your new layout is saved under
MyAssays/Layouts in your MyAssays folder root.
Creating Multiple Plate Layouts
A layout can be defined for each real-world physical container. The Layout Editor can be used
to define layouts across multiple containers. The group numbering of samples is contiguous
and sequential for each type.
There are a number of different ways that standards and samples of an assay might be
arranged across multiple plates. Layout Editor allows you to specify for each plate:
1. How samples are arranged
2. Which standards measurements should be used to construct a standard curve
3. Which curve to use to calculate concentrations from
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Layout Editor Settings
With the following options, you can specify how samples are arranged on a plate.
There are two modes for specifying an assay layout:
l Erase – For removing samples that you are not measuring
l Fill – For defining the layout by selecting areas to fill
Erase Edit Mode
Erase mode allows you to remove samples that you are not measuring (i.e., mark them
Unused).
To remove a position:
1. Select the Erase icon on the toolbar.
2. Move the mouse pointer over a defined position.
3. Left-click to remove all positions of this sample group.
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If the sample group contains more positions than you want to remove, select
Unused type on the type and group selector and fill the position(s) with it.
Sample numbering will be adjusted to ensure that the group numbering remains contiguous.
Fill Edit Mode
Fill Edit mode allows greater flexibility in layout arrangement. To enter Fill Edit mode, press
the Fill button. The Fill Settings button will appear, enabling you to specify how the positions
you select are filled.
Also, the type and group selector can be used to select the next group to fill with your chosen
settings.
To change the sample at a position, simply left-click your mouse pointer on the position. To fill
an area, left-click at the first position and hold and drag to the last position. The Fill Settings
dialog window appears, allowing you to change how this fill operates. You can specify the fill
direction and number of positions for your selected group. The following fill options are
available:
l By Row
l By Column
l Rectangle Mode
l Snake Mode
If Rectangle Mode is selected, you can choose whether the replicates fill By Row or By
Column. You can specify the number of positions per group using the entry box.
Group numbering must start at 1 and be contiguous for each sample type.
Undo/Redo
Use these buttons to cancel the last change or re-apply it.
Save + Close
When you are satisfied with your changes, press the Save + Close button. This will save the
new layout to your Layouts tab and make it available to relevant assay analyses.
Multiple Plate Options
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If you have selected the Multiple Plate option or are editing a multiple plate layout, the Layout
Editor's functionality expands with options applicable only to multiple plate layouts. Also, you
can switch between plates and choose curve-fit generation properties.
Options
The toolbar for multiple plate expands with following options:
l Add Plate – Adds an empty plate
l Remove Plate – Removes a selected plate
l Duplicate plate – Copies a selected plate with all defined changes
Curve Selection
Curve selection describes how the multiple layout is arranged and behaves (i.e., defines if and
how a curve can be selected).
Multiplex Options
For multiplex assays, only single layouts can be applied.
Layout Configuration
The Layout Editor provides a method for selecting the curve to use for each plate. The layout
configuration defines how the standard groups are arranged on each plate. This can be the
curve generated from either:
• the average (of calibrator groups measured across separate plates)
• a specific plate
Example
With a layout that comprises of 3 plates, the following options for creating a curve are
available:
l Standard (Average Across Plates)
l Standard (Plate 1)
l Standard (Plate 2)
l Standard (Plate 3)
Partial Standards
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When running an assay across multiple plates, you might decide that it is not necessary to
measure the full range of standards on every plate. Instead you could read the full range of
standards on only one plate and measure a subset of the standards on other plates, then
visually compare the curve fits to ensure they are usable. This allows you to:
l Save costs on standards
l Save preparation time
l Measure more samples per plate
MyAssays makes this process easy by allowing you to define layouts with partial standard
groups. We use this term to define a layout that includes the full range of standards on one or
more plates of the layout with other plates featuring only a subset of the full range of
standards.
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Multiple Plate Group Numbering
When defining the layout (in the Layout Editor), the numbering for each sample group on
each and every plate should always start at 1.
This might seem counter-intuitive, however, this is intentional to make things easier when
working with multiple plates. With this approach, if you decide to change the number of
samples on the first plate you do not need to update the numbering on all plates that follow.
When the analysis is performed, the software will apply the group numbers across plates
contiguously. For example, if the layouts for plate 1 and plate 2 are both defined with
Unknown1-Unknown10, when the analysis is performed, plate 1 will include Unknowns110 and plate 2 would include Unknowns11-20.
Analysis
Versioned Analysis
Overview
When you launch an assay protocol and press the Calculate button, an assay-results file is
created with the first calculated version of results (Version 1). A result version contains all of
the settings and results for a specific Calculation. You can select to view a different version of
results at any point.
Each time the Calculate button is pressed, a new version of results is created and
stored to the .assay-results.
Changing Versions
When there is more than one version of results available, you can freely switch between them.
How to: Switch Between Versions of the Results
1. Open a results file.
2. Hover to a Versions Group on the Home tab of the MAA Ribbon.
3. Select the Version you need from Current version drop-down list.
Changing the version of results updates all settings and results views to display the data
relevant for the selected results view. Any changes made to the currently selected version will
be saved as a new version.
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Pressing Calculate will create a new version (with version number 1+ the current
highest version number).
For example, if there are 3 versions of results and you go back to Version 1 and make
changes, then Version 4 (pending) is created. If you then press Calculate, the Version 4
will be stored. This is the new set of results based on Version 1 with those changes made to
Version 1 (as Version 4).
Purge Versions and Rollback
Purge Versions
The Purge operation will delete all version data except the current selected version. This
setting is available if your assay contains more than one version.
Rollback
The Rollback operation will delete all data that follows the selected version. It is active if the
current version is not the newest version of the results.
The Purge Versions and Rollback operations cannot be undone; however, the
changes are not committed to the file until the Save operation is performed.
Uncalculated Version
When you make changes to a results file after the last set of calculations and before the
Calculate button is pressed again, the Version {n} (pending) label is displayed in the
Current Version selector.
Unsaved Changes
If you made changes without calculating them (i.e., a pending version) and want to exit MAA,
you will be advised to Save these settings on exit. These settings will be saved to the assayresults file as new uncalculated version and also to the assay protocol if that option is selected.
This also applies to assay protocol with no calculated changes.
When the assay-results file with uncalculated changes is opened in MAA, the first set
of results will be calculated automatically.
Save Options
You can save data you are working with as an:
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l Assay protocol – To save only the settings representing the assay analysis to be
performed
l Assay results – To save the settings representing the assay analysis and versioned results
of its performance
Save Changes To Protocol
This checkbox appears if you have launched MAA with an assay protocol file. It indicates
whether the changes (i.e., layout to use, measurements data, Sample IDs) should be stored in
the assay-protocol file, overwriting its default properties.
The Save Changes to Protocol state (checked/unchecked) persists between
sessions.
Save Commands
l Save
To simplify the different types of save options, there is one master Save command.
The master Save command is accessible from:
l Quick Access Toolbar
l File | Save BSA
This option saves results of the analysis to the directory it was launched from. It also
optionally saves changes to a protocol, if the Save Changes to Protocol checkbox is
checked.
l Save Results As
With this command you can save the assay results with all changes to a new .assay-results
file. When you press the Save Results As button, a dialog box appears. You can provide a
new file name and a location for it to be stored in.
l Save Protocol As
With this command you can save the modified assay protocol to a new .assay-protocol file.
When you press the Save Protocol As button, a dialog box appears. You can provide a
new file name and a location for it to be stored in.
Naming Results
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This setting is only available if MAA was launched with an assay-protocol file.
With this you can specify a custom target path to save a file to and how save filenames are
automatically generated (e.g., to arrange all results into subfolders based on the protocol
name).
When the Results Naming button is pressed, a dialog window with the following options
appears:
1. Default (automatically named and stored in Results folder).
This means a default file naming convention is used and applied by default; this default
method is based on the protocol name of the related assay protocol. Example: For
“ELISA.assay-protocol,” the first time it is ran a results file “ELISA (1).assay-results” is
created in the Results folder.
2. Custom
You can customize the naming method and target path manually, by using macros, or
by combining these approaches.
How to: Set Custom Results Naming
If you choose the Custom option in the Results Naming dialog window, the following
textboxes will appear:
Folder. With this you can provide a custom path to store your files in. The options are:
l Provide a full path to the folder in the textbox (manually or from clipboard).
l Click the Browse... button to provide the full path to the folder in Browse for Folder tree
view. Press OK to save the path or Cancel to close the dialog box without any updates.
l Click Manage... to insert any number of supported macros from the list. Press Update to
save changes or Discard to cancel.
Also, you can combine these options. Example: Provide path to a parent folder via the
Browse... option and use the <ProtocolName><CurrentDateShort> macros from Manage...
to specify folder name.
Filename. This specifies the title of assay results files. The options are:
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l Type a name in the textbox.
l Click Manage... to insert any number of supported macros from the list. Press Update to
save changes or Discard to cancel.
Also, you can combine these options. Example: type in "Experimental results for" and use
<ProtocolName><CurrentDateShort> macros from Manage... to specify the analysis type
and date it was performed.
If the Test settings when OK is clicked checkbox is enabled, MAA checks the
accessibility of specified target path and verifies if the file can be created.
When these are provided, press OK to apply changes or Cancel to close the dialog box
without any updates.
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New Protocol Wizard
How to: Launch New Protocol Wizard
1. Launch the MADE and do one of the following:
2. Select the New Protocol... option on MADE Welcome Screen.
3. Select the Protocols tab and click on the New button of File group.
The New Protocol Wizard provides a way to easily create a new assay protocol by entering
only the minimum and most important information.
It guides you through the steps you can move between by clicking Next and Back buttons to
configure the following assay parameters:
l Measurement data specifics
l Layout and sample types settings
l Administrative properties (meta data)
l A name and (optionally) a description for the protocol
At any step, you can click on the cross icon at the upper right of the New Protocol
Wizard window to discard all changes and exit.
At the end of the wizard, the .assay-protocol file with defined configuration is added to your
default Protocols folder.
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Analysis Launcher
The Analysis Launcher tool simplifies building up the command line arguments to launch a
protocol with a specified set of options in MAA. It provides visual controls and validation to
predetermine data analysis with no user intervention required. Also, it comes in handy when
configuring multiple runs of a certain data analysis (via assay protocol) with various
measurements data.
The Launcher includes:
l Controls for browsing and selecting an assay protocol, measurement data, Sample IDs, and
protocol tweaks files
l Various user-friendly controls for selection of command line arguments
l A textbox showing the full command line of the current selection
l The Copy to clipboard button, which copies a built command line with selected controls
to the clipboard
l The Launch button, which launches the command line
The built command line is displayed as changes are made. When valid, the command line can
be launched directly from Launcher.
How to: Open Analysis Launcher
You can open this tool in one of the following ways:
l Press the Analysis Launcher button on the Tools tab in the Utilities group of the MADE
Ribbon.
l Enable the Launch Assay Protocol with Options feature and press the Launch Assay
Protocol with Options button.
l Launch the MyAssays.Desktop.Analysis.Launcher application under the
MyAssays/MyAssays Desktop/ installation root folder.
If you run the application from the installation root, specify the protocol file path
to see all applicable controls.
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Common Tasks
Data Storage
Storing Data
The default path for saving your data files lies in your Documents folder, under the MyAssays
subfolder. You can open it in MyAssays Desktop Explorer, Windows Explorer, or any other
content-managing application.
You may want to change the way your data files are stored. For example, you may prefer your
data to be stored in a network location or in a shared folder that is shared with other users of
the system.
How to: Set an Assay Data Folder Root
In the MADE Ribbon, select Backstage File Screen| Data. Press the Select
Folder Root button and choose the preferable folder.
Uninstalling MyAssays Desktop will not remove your data files.
Backing Up Data
Periodical backing up of your assets is beneficial so you will always have a copy of all
important data, and is a part of responsible PC administration. To facilitate your backup
process, all data files are stored under a single parent data folder that should be backed up.
To determine which folder to backup, launch MyAssays Desktop Explorer and select File |
Data to obtain the current parent directory. All relevant data is stored in the MyAssays
subdirectory of this folder.
Data Import
Visualizing Data
Heatmap Plate View
The Heatmap View displays a plate layout colored in accordance with the measurement data
numerical values. For example, data with smaller numerical values is depicted in dark green
and data with larger numerical values is shown in red.
How to: Access the Heatmap View
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1. In MAA, select the Measurements or Microplate tab. Check
2. Check the Heatmap View option in the lower left corner of the right pane.
Heatmap view is relevant for endpoint data only.
Structure
Heatmap View comprises:
l Plate preview with the color of each position based on its numerical value converted to a
color within a given scale
Data for Unused positions is not displayed; the wells appear white and empty.
l A heatmap scale (located under plate preview) with clickable minimum and maximum
controls
l A scale function menu that determines the heatmap scale type (linear or Log10)
l The auto scale option
l 2D/3D view buttons
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Heatmap View Settings
Minimum and Maximum Controls
These settings allow you to set the color scheme range displayed on the heatmap plate
preview. Each position on the layout is colored according to its numerical value and the color
scale (using the color range min/max values). Values outside the range are rounded to the
nearest min or max color.
How to: Change the Minimum Value
1. Hover and click on the minimum value on the left side of the color bar to edit the
heatmap minimum value.
2. Type in a minimum value for the heatmap (e.g., 0.05).
3. Click on the check icon to save your changes or on the cross icon to cancel them.
If you type in a minimum value that is greater than the maximum value, an error
message will be displayed.
How to: Change the Maximum Value
1. Hover and click on the maximum value (right side of the color bar) to edit the max value.
2. Type in a maximum value for the heatmap (e.g., 2.00).
3. Click on the check icon to save your changes or on the cross icon to cancel them.
For multiple plate multiplex assays, consider all used positions before you type in
min and max values for the heatmap.
Scale Function
With the Heatmap View, you can use one of the following scale settings for heatmap
generation:
l Log 10 scale – Minimum and maximum control values change based on a logarithmic
function
l Linear scale – Minimum and maximum control values change with a constant speed
How to: Switch Between the Log10 and the Linear Scales
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1. With the Heatmap View turned on, click on the Scale function button (from the
righthand side of the heatmap maximum control) to display the scale settings.
2. Select the scale you want the Heatmap View to display with:
l Log10 scale – Click on the Log10 scale button (right one). The heatmap is
refreshed to display a Log10 color scale.
l Linear scale – Click on the Linear scale button (left one). The heatmap is refreshed
to display a linear color scale.
Auto Scale
The Auto Scale setting allows you to automatically scale the plate preview, if the minimum
and maximum values are changed for a set of measurement data. For example, if you type a
custom maximum value in the heatmap maximum control and then change the measurement
data, the Heatmap View will be updated with the original scale.
If you select the Auto Scale and there is a change in the measurement data, the Heatmap View
is updated using the new scale. In this case, if the edited measurement data has a new
min/max, then the heatmap scale is updated accordingly.
How to: Access the Auto Scale
1. With the Heatmap View turned on, click on the Auto Scale button (on the right side of
the heatmap maximum control).
2. Click on the Auto Scale button.
You can toggle the Auto Scale setting on and off by clicking on the Auto Scale
button.
2D/3D View
Similar to the Layout View, you can view the heatmap in a 2D or a 3D format.
How to: View the Heatmap in a 3D Format
1. In MAA, select the Measurementsor Microplate tab.
2. Check the Heatmap View option in the lower left corner of right pane.
3. Click the 3D button.
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You can toggle between the 2D and 3D view using the corresponding buttons.
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Flagging/Outlier Exclusion
Overview
Flagging is used to mark and indicate data to exclude from further analysis. This can be used
to identify outliers to ignore issues such as poor measurements or data for incorrectly
prepared or contaminated samples.
There are two types of data that can be flagged:
l Position
l Point
For all measurements types, any position can be marked as flagged (e.g., position A1). When
the data at a position is flagged, any reference to the sample contained at that position will not
include the flagged position's data.
For example, if Standard1 comprises of 3 replicates measured at position A1, A2, and A3, if
position A1 is flagged, Standard1 would refer to only the not-flagged values (i.e., the average
of the data at positions A2 and A3).
For kinetic or spectral data, any specific data point can be marked as flagged too (e.g., the data
point for cycle 1 of position A1). Any flagged data points are not included in any further
analysis. If all points of a position are flagged, then the position is considered to be flagged.
For example, if the kinetic measurement data at position A1 comprises of 10 cycles, if point 1
is flagged, then a kinetic reduction operation is performed (such as computing the average of
kinetic cycles in XY Reduction Average) and the calculations would ignore the data of the
flagged point.
Data can be flagged interactively or by calculations. Any flagged data will be indicated in the
report and export accordingly. It is also possible to customize the labels for flagged data in the
report.
Interactive Flagging
Position or point data can be flagged (and "unflagged") interactively by left-clicking the mouse
on the position or data point to flag. This is possible in:
l Measurements View (in the Measurements tab)
l Standards Chart (in Results tab when working with Standard Curve Fit transform)
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l Kinetic/Spectral Measurements Chart (in the Measurements tab | Charts subtab when
working with kinetic/spectral data)
l Kinetic/Spectral Analysis Chart (in the Results tab when working with Kinetic/Spectral
transforms)
How to: Flag Data Position with Measurements View
1. In MAA, select the Measurements, Microplate, or Sample IDs tab.
2. Hover to the Plate Preview pane on the right of the selected tab.
3. Position the mouse pointer over the point to flag and left-click.
A flagged position appears on the Measurements View with a cross-through it.
How to: Flag Data Position with Standards Chart
1. Add the Standard Curve Fit Transform:
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to
open it.
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3. Press the Add button.
4. Select the Standard Curve Fit transform.
5. Press the Create button.
6. Review or edit the transform's input matrix and output matrix name as
required.
7. Provide your required settings for the transform (see the Properties section
above).
8. Press the Calculate button.
2. Select the Standard Curve Fit tab of the Results tab. If your assay comprises
multiple plates, select the one that contains the analysis view for your data.
3. Select the sample type on the legend to view how the points are spread on the curve.
4. Position the mouse pointer over the point to flag and left-click.
1. Press the Calculate button to perform the calculations with the new settings
and to see the results.
Any flagged item can be "unflagged" by left-clicking on the flagged item.
How to: Flag Kinetic/Spectral Data Point with Kinetic/Spectral
Measurements Chart
1. In MAA, select the Measurements tab.
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2. Select the Chart tab.
3. Use the Select Group/Select Position controls to select the required data.
4. Position the mouse pointer over the point to flag and left-click.
How to: Flag Kinetic/Spectral Data Point with Kinetic/Spectral Analysis
Chart
If your data analysis includes an Analysis View (used to display further information about
the performed data analysis), then you can flag XY points directly from this view.
1. In MAA, select the Results tab.
2. Select the tab that contains the analysis view for your data.
3. Position the mouse pointer over the point to flag and left-click.
Any flagged item can be "unflagged" by left-clicking on the flagged item.
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Flags View
If there is any flagged data, then the Flags option will appear in the View group of the Home
Ribbon tab. The Flags view control will display a list of the currently flagged items (including
both positions and point data if relevant).
Toggle the status of an entry to change its flagged status.
Calculated Flagging
In addition to manually selecting which data to flag, calculations may result in flagged results.
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For example, the Replicate Outlier Removal transform can be used to automatically flag
sample replicates that are outside a definable expected range. For more flexibility, the Auto
Flag transform enables you to define conditions to flag endpoint values.
When working with kinetic or spectral data, the XY Outlier Removal transform can be used to
automatically detect specific data points that deviate from an expected pattern beyond an
expected threshold.
Also, for certain calculations there might not be a valid result for the calculation. In those cases
the result will be marked as flagged (as no further calculation is possible).
Advanced
All transforms include a setting under the Advanced properties group for selecting which
sample types the transform should calculate results for. For those samples that are not to be
calculated, the results can be set to be flagged as either:
l Flagged with Value
l Flagged no Value
Flagged Data in the Report
The report will indicate flagged data as strikethrough text (such as 1.23 or Flagged). Where
data has been flagged and there is a value associated with it, the original value appears in
strikethrough.
Where there is no value associated, the text Flagged appears (also in strikethrough).
For example,
Here, position A3 has been flagged. In this case the calculation of the concentration will use
only the value from position A4.
Here the Raw values in positions A3 and A4 have been flagged. This means that there is no
result for the Concentration.
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Flagged Data in Text-Based Exports
For text-based export (such as CSV, TXT) where strikethrough is not available, flagged data is
identified by asterisk symbols surrounding each flagged data item, for example:
*1.23*
*Flagged*
For example, the following export text shows three data values flagged:
Sample,Positions,Raw,Concentration
Unknown1,A3 A4,*1.599* *1.665*,*Flagged*
Custom Flag Label
If the default strikethrough text "Flagged" is not suitable, you can specify your own text to use
instead. For example, here the text No result is used instead of Flagged.
This is an advanced setting that can only be configured through the XML editor. For more
information please refer to Custom Flag Label configuration.
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Custom Flag Label Configuration
The Custom Flag Label feature enables specific labels to be defined and used to indicate
flagged results instead of the default strikethrough flagged text (such as 1.23 or Flagged).
The settings are available at different levels of the Matrix-Transform Analysis configuration;
this provides a fine degree of control over how flagged results are reported. For example, you
may prefer to label flagged raw data differently than flagged concentration results.
Additionally, the Standard Curve Fit transform supports conditional flagging and applying
labels. This enables the definition of rules to apply that define how to flag and label results in
accordance with calculations from the curve. For example, you may want to flag or use a
custom label for samples that fall outside a defined concentration range. This is described
further in Standard Curve Fit Custom Flag Labels.
To label sample results that satisfy specified conditions without flagging them, please
refer to the Sample Labels topic.
Custom Flag Label Levels
Flag labels can be set at the following levels:
1. By Transform Calculations
2. By Output Matrix
3. General
If there is a combination of custom flag labels defined, then they are handled in the order
given above. Custom labels higher in the list take precedence over those further down the list
(e.g., any specified in the transform implementation override any defined at both the output
matrix and general level).
XML Configuration
These advanced settings are only available through the XML editor. To access the XML editor,
select the XML tab in the Assay Properties panel.
Custom Flag Label: General
The general custom flag label is the label to use in place of the default Flagged label. Note,
when specifying custom flag text, strikethrough is not applied.
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You can set the general custom flag label at the Transforms root level. All flagged data
positions on any matrix (raw or calculated) will then use this setting. Enter the flag label with
the following syntax:
<Transforms FlagLabel="No result" >
With this setting, the flagged data will appear as follows:
How to: Set General Custom Flag Label
1. Select the XML tab in the Assay Properties panel (If the XML tab is not immediately
visible, press the << Show All Properties button to expand).
2. Locate the Transforms element.
3. Add the FlagLabel element to the Transforms element.
Custom Flag Label: By Output Matrix
A custom flag label can be set on any output matrix of any transform. This defines a common
flag label to use for all data positions flagged by the transform.
Provide the label with the following syntax (editable label highlighted in yellow):
<Matrix Id="Calc1"Name ="Calc1" FlagLabel ="Outlier Removed" />
This setting only applies to data points flagged by the transform.
How to: Set Custom Flag Label for an Output Matrix
1. Select the XML tab in the Assay Properties panel (If the XML tab is not immediately
visible, press the << Show All Properties button to expand).
2. Locate the Transforms element.
3. Locate Output element.
4. Add the FlagLabel element to the Matrix element.
Custom Flag Label: By Transform Calculations
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The Standard Curve Fit transform is the only transform that provides custom flag settings by
calculation. For more details, see Standard Curve Fit Custom Flag Labels.
Custom Flag Label Reporting
The specified custom flag labels will be used in the report and export; however, the Sample
Table Replicates setting of matrices configuration also impacts which labels are displayed.
This is explained as follows:
l List
For any flagged position that has a custom flag label associated with it, that custom flag
label is displayed.
l Average
In this case the flag label is displayed only if all positions in a sample group are flagged;
otherwise, average of non-flagged data points is calculated. For the former, the following
scenarios apply:
1. If all positions have no custom flag label then the default flagging settings are used.
2. If all positions have the same custom flag label, associated with them, then that
custom label is displayed.
3. If positions of a sample group have different flag labels associated with them, then
the list of flag labels (custom and/or default) are displayed in the order of the
positions. Example: If there are 3 positions in a group and they are all flagged (the
first has no custom label, the second has custom label “Label A,” and the third has
custom label “Label B”), then “Flagged, Label A, Label B” will be displayed.
l First
Any custom flag label associated with the first position is displayed.
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Matrices
Generally, all data in MyAssays is stored in matrices. Each matrix can contain one or more
replicates, and for each matrix certain relevant transforms can be applied. In the Matrices
Tab, the settings for each of these matrices can be configured.
How to: View Matrices Tab
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to open it.
3. Select the Matrices tab (If the Matrices tab is not immediately visible, press the <<
Show All Properties button to expand).
Matrix Selection Pane
This pane includes available matrices in a tree list. This list contains two basic branches: Raw
and Calculated.
Raw
This is input data that is stored in the Measurements Tab. The quantity and type of Raw
matrices depends on the assay type and configuration.
Click here to learn more about measurement data.
Calculated
This consists of all new matrices that are created and added to output by transforms.
Matrix Configuration Pane
Each input and each output matrix of a newly added transform is automatically included in
both Sample and Matrix Tables. With the following settings, you can configure whether and
how they are displayed.
General
Name
A title to be used for a matrix in the headers of the Sample Table and Matrix
Table. If it is not specified, then the default title is used.
Numerical Formatting
Precision
The number of digits to be displayed for a numerical value. The interpretation
of Precision depends on the Type setting.
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Specifies how a numerical value is formatted with regard to precision and
rounding options:
Type
l Significant Figures – The value is reported to the specified number of
significant figures. This accepts Precision values in range 1 through 14.
l Decimal Places – The number of digits to display to the right of the
decimal point. This accepts Precision values in range 0 through 14.
Report Contents
Include in
Matrix
Tables
Defines whether the associated matrix should be included in the Matrix Tables
Include in
Sample
Tables
Defines whether associated matrix should be included in the Sample Tables
Matrix
Table Ids
Defines in which Matrix Table(s) the associated matrix should be included.
Sample
Table Ids
Defines in which Sample Table(s) the associated matrix should be included.
Determines how replicates of each sample group are included in the Sample
table:
Sample
Table Replicates
l Average – The average value of all replicates is displayed. This applies to
Endpoint data only.
l List – All replicate values are included in the table.
l First – Only the first value is displayed. This simplifies the view of
transforms that result in the same values for all replicates of each sample
group (e.g., Average, XY Replicates Average).
Configuration of Endpoint Matrices
For endpoint data you can additionally configure the following settings to apply on the report.
Sample Labels
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If this is specified, the Condition is evaluated for each position of the associated matrix and
Label is displayed in place of the value for the position. For further details on this feature see
Sample Labels topic.
Value Bars
Within this category you can configure whether and how to fill replicate values with color for
any Raw or Calculated endpoint matrix.
The value bars are created using the minimum and maximum replicate values of the matrix.
These are treated as 0% and 100%, respectively, and filled in accordingly.
Display
Value Bars
Defines whether this feature is used in the Sample Tables.
Defines how to fill in the value bar. Select one of the following types:
Fill
Solid – To fill value bars self-colored with Fill Colour.
Gradient – To fill value bars with the gradation from Fill Color in the left to
white on the right.
Fill Colour
Click on the drop-down to open a color picker.
It defines how to fill in the value bar's border. Select one of the following
types:
Border
Solid – To fill value bars self-colored with Fill Colour.
None – To not display the Border.
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Sample Labels
The Sample Labels feature can be used add your own text in-place or alongside measurement
or computed numerical values. Also, your data or labels can be highlighted to visually
enhance the report output; this is useful to draw the reader's eye to specific areas of interest
or to color code your report content.
Samples are reported with label and/or colour in accordance with the rules that you specify.
You can define as many rules as you need. If a data point satisfies multiple
conditions, then all corresponding labels are displayed in the report.
The rules for sample labels are applied to the corresponding data included in the report. In the
Matrix Table report element, the label or coloring would apply to each position. In the Sample
Table report element, this might be each individual replicate value or each sample's average; it
depends on how the data is configured in the report output.
For example, here is a Matrix Table output highlighting all sample measurements > 1.5 in
yellow.
With the Sample Table report element, each matrix can be configured to display the average
of the sample group or list each replicate. (This can be set with the Sample Table Replicates
setting in the Matrix configuration.) In either case the sample label will be applied to the
selected data.
For example, the following Sample Table has been configured with the Raw data listing all
replicate values. In this case the label is applied to each replicate that satisfies the rule.
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In the following example, the Sample Table has been configured with the Raw Data listing the
average of the replicates values. In this case the label is applied to the computed average.
This feature is useful for detecting and reporting samples that are outside expected ranges.
For example, you might want to highlight or label samples whose concentration, response, or
%CV is outside a specific range.
Applying label, unlike flagging, does not exclude data from calculations, nor does it
change the actual value used in subsequent calculations. It simply replaces their
reported values with a specified Label and applies any colouring.
How to: Apply a Sample Label
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to open it.
3. Select the Matrices tab (If the Matrices tab is not immediately visible, press the <<
Show All Properties button to expand).
4. Select one of the numbered matrices from under either the Raw or Calculated branch,
which contains the data to label.
5. Click on the drop-down Label property of the Matrix Configuration pane to view Matrix
Sample Label Editor.
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6. Enter the condition (in terms of x) along with the label and any highlight or text coloring
to apply.
7. Press Add to add further rules. Click outside the Editor when you have finished.
8. Press the Calculate button to perform calculations with the new settings and to see the
results.
In the Matrix Sample Label Editor, you can add and remove any number of rules and move
them up or down with the corresponding arrow buttons.
The rules are specified with the following editable text box properties:
l Condition – A Boolean condition to evaluate (typically a conditional
expression) in terms of x. The x value specified in the expression refers to
the sample.
Label
l Label – The label to be displayed in the Sample Table and Matrix Table, if
the Condition is satisfied. Use {x} to refer to the data value that was used
in the condition (i.e., the original data point).
l Highlight – This sets the background color for the cell when Condition is
satisfied. Where there are multiple Conditions satisfied, the color of the
first satisfied Condition is applied.
l Text – This sets the text color for the cell when Condition is satisfied.
Where there are multiple Conditions satisfied, the color of the first
satisfied Condition is applied.
If the Condition is satisfied, the replicate will be reported with the corresponding Label in
place of its value.
Reporting Data with Labels
The results with labels appear in associated matrix's Sample Table and Matrix Table.
For example,
Here the Condition x>1 and Label >1 are specified for Concentration matrix. In this case the
calculation of the concentration for positions A3 and A4 is reported as >1.
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Where label has been applied on the data and there is a {x} reference associated with the
Label, the numeric value appears in the Label, i.e.:
Sample Labels Examples
Condition
Label Applied If
x<1
The value is greater than 1.
and(x>0,
x<1)
The value is in the 0–1 range.
x<Control1 The value is greater than mean of Control1 sample group.
x>TOV1.d
The value is greater than the d coefficient of the 4PL curve-fit (assuming that
the 1st transform is a Standard Curve Fit transform using the 4PL curve-fit).
See Transform Output Variables for more details of how to refer to such
results.
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Validation
MyAssays Desktop's Validation features can automatically check that measurements and
results are within expected ranges. Any configured validation tests are performed as part of
each assay run with the results (pass or fail) being clearly presented as part of the output
report.
For example, a validation can test that the %CV of the replicates of all sample groups is less
than 15.
Validation Definition
A validation is defined using a Boolean validation condition. If the condition evaluates to true,
then the validation rule is considered to be satisfied and the result for the validation is Pass. If
the condition evaluates to false, the validation test result is Fail.
The validation condition is specified using an expression along with a matrix reference and
type. For example, the validation condition expression:
x < 15
When associated with the %CV matrix and evaluated on all groups will check that the %CV of
all sample groups is < 15.
Validation rules are arranged by validations tables. You can add as many validations rules as
you need to each validation table. You can create as many validations tables as you need. It is
useful to group related validations into validations table accordingly.
Each validation table is a separate report element so can be configured separately.
For example, you might want each validation table to appear on a different sheet in
the report.
Working with Validations
The following sections explain how to add, define, and review validation rules. Also, example
validation expressions are provided:
How to: Add Validation to an Assay
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to open it.
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3. Select the Validation tab (If the Validation tab is not immediately visible, press the <<
Show All Properties button to expand.)
4. Press the Add button on Tables pane.
5. Review and edit the validation's properties as required.
Validation Configuration
The Validation tab consists of three panes:
l Tables – For managing the validation tables.
l Conditions – For managing the validation conditions.
l Condition Configuration – For specifying a validation condition.
Tables
The Tables pane defines the validation tables that are available for inclusion in the report.
Each validation table is given a unique identifier (e.g., ValidationsTable1).
Use the Add and Remove buttons to define the number of tables required.
You may prefer to include all conditions in one single table or arrange conditions
between multiple tables.
The ordering, settings, and inclusion of each Validation Table is controlled via the
Report contents.
Conditions
The Conditions pane defines each validation condition for the selected validations table.
Use the Add or Remove buttons to add conditions as required. Use the Up and Down buttons
to set the order in which the conditions will appear in the table.
For each condition, a description can be specified under the Description column. Use this to
provide a clear textual description; this will be used in the report to convey what has been
validated.
For each condition, select which Matrix the condition applies to.
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For example, if you plan to validate measurement data (i.e., a Raw matrix), select the Raw
matrix to validate. Any of the raw or calculated matrices that contain endpoint data can be
validated. Also, it is possible to validate non-matrix data (such as transform output variables).
If there are no available matrices, then the (NA) label appears. In this case validation
conditions can still be created that do not refer to endpoint matrix data.
Validation conditions are not applicable to XY data matrices. To validate XY data, use
an XY reduction transform and validate the endpoint result.
Conditions Configuration
The Conditions Configuration pane is used to specify the selected validation's condition.
The Expression setting is used to define the validation expression Boolean condition. This will
typically be an expression with a logical operator and perhaps a conditional function.
The interpretation of the expression depends on the Type setting:
l Position – The expression is evaluated for every position on the matrix of every container.
l Group – The expression is evaluated for every sample group on the matrix of every
container. (A sample group refers to the average of the replicates that belong to the group
that are not flagged).
l Single – the expression is evaluated once (useful for checking specific sample groups).
Single type expressions are the simplest; they define a single expression that is evaluated
once on the specified matrix of each container.
For example, Standard1 < Standard2 is an example of a Single type expression that
validates that the value of Standard1 is less than Standard2 on the specified matrix. (For
multiple plate assays, this condition will be evaluated on every plate.)
For Position or Group expressions, the expression will include an x reference, and in these
cases the x variable refers to each position or each group. For example, if you wanted to check
that the value at every measured position in a matrix is greater than 0.5, then use the
Position setting with the expression: x > 0.5. In this case every data point (on the selected
matrix) is validated.
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Similarly, if you wanted to check that the average value of each group in a matrix is less than
3, use the Group setting with the expression x < 3. In this case the average of every sample
group (on the selected matrix) is validated.
The Types setting is used to specify which sample types a Position or Group expression will
be evaluated on. By default, these validation expressions are evaluated for all sample types. If
you only want to test specific sample types, then use this setting to select which types to
validate.
Validating Non-Matrix Data
In addition to validating matrix data, it is also possible to validate other types of numerical
data. For example, you may want to verify that the R2 value of a curve fit is greater than a
specific threshold. These types of validations can be defined using the Single type and
referring to the data to validate within the expression. This will typically be using a Transform
Output Variable (TOV). The TOVs that are available will depend on your configured
transforms.
For example, the expression TOV1.R2 > 0.9 will check that the R2 value of the curve fit (as
used in the first transform) is greater than 0.9.
Validation Results
Validation conditions are checked when the data analysis is performed (after all transform
calculations).
To apply any new or modified settings, press the Calculate button and inspect the report to
see the results. Each validation test verifies the result in terms of the specified Boolean
condition.
For Position and Group validations, the result is only Pass if all evaluations evaluate
to true.
The results will appear in the associated validation table in the report and will be marked as
Pass, Fail, or Inconclusive accordingly:
Pass
If the validation condition is evaluated as true (the numerical result not equal 0).
Fail
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If the validation condition is evaluated as false (the numerical result equals 0). For
Fail, the whole validation table row is highlighted with yellow background.
Inconclusive
If the validation expression could not be evaluated (e.g., a syntax error in the
expression), the result is treated as Inconclusive with the error message shown in
Results column (in red text).
All validations are evaluated (irrespective of whether they are included in the report).
You may configure how and if the Validation Tables are reported. In any case, the
results of each validation test are listed in the Calculation log.
Examples
Here are examples of validation rules along with descriptions of their behavior. They apply the
validation on the selected data matrix.
Type
Condition
Validates
Position
x < 2.5
All positions (that are not flagged) have a value
less than 2.5.
Position
and (x > 0, x < 2.5)
All positions (that are not flagged) have a value
less than 2.5.
Group
x < 2.5
The average of the replicates (that are not flagged)
of all sample groups is less than 2.5.
Position
and (x > 0, x < 2.5)
The average of the replicates (that are not flagged)
of all sample groups is greater than 0 and less
than 2.5.
Single
Control1 < 1.5
The average of Control1 replicates (that are not
flagged) is less than 1.5.
Single
sd(Control1)<10
The standard deviation of the Control1 replicates
(that are not flagged) is less than 10.
Single
pcv(Control1)<15
The %CV of the Control1 replicates (that are not
flagged) is less than 15.
Single
median(Control1)>0.75
The median of the Control1 replicates (that are not
flagged) is greater than 1.5.
Single
TOV1.R2 > 0.9
The R2 value of the curve fit (as used in the first
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Type
Condition
Validates
transform) is greater than 0.9. (The R2 value is
referenced using a Transform Output Variable).
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Evaluation
MyAssays Desktop's Evaluation feature can be used to calculate and present specific
quantities of interest in the report. For example, the limits of detection of an assay could be
computed and reported using an evaluation.
Evaluations are expression-based calculations arranged as Evaluation Tables for inclusion in
the report. Any value that can be referenced or computed can be included in an Evaluation
table.
Unlike the Sample Table or Matrix Table report elements (which display the results of a
calculation performed on each sample), an Evaluation Table reports calculations that are not
computed for every sample.
If you want to perform a calculation with a result for every sample, instead of using
an evaluation, use a transform with the results for each sample displayed in the
Sample Table or Matrix Table.
Evaluation Definition
A single evaluation is defined using an expression and can be given a description to
accompany its result.
Evaluations are arranged by Evaluation Tables. You can add as many evaluations as you need
to each Evaluation Table. You can create as many tables as you need. It is useful to group
related evaluations into Evaluation Tables accordingly.
Working with Evaluations
The following sections explain how to add, define, and review evaluations. Also, example
evaluations expressions are provided.
How to: Add an Evaluation to an Assay
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to open it.
3. Select the Evaluation tab (If the Evaluation tab is not immediately visible, press the <<
Show All Properties button to expand).
4. Press the Add button on Tables pane.
5. Review and edit the evaluation's properties as required.
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Evaluation Configuration
The Evaluation tab consists of three panes:
l Tables – For managing the evaluation tables
l Evaluations – For managing the evaluations
l Expression Configuration – For specifying the evaluation expression
Tables
The Tables pane defines the Evaluation Tables that are available for inclusion in the report.
Each table is given a unique identifier (e.g., EvaluationsTable1).
Use the Add and Remove buttons to define the number of tables required.
You may prefer to include all evaluations in a single table or arrange evaluations
between multiple tables.
The ordering, settings, and inclusion of each Evaluation Table is controlled via the
Report contents.
Evaluations
The Evaluations pane defines each evaluation for the selected evaluations table.
Use the Add or Remove buttons to add evaluations as required. Use the Up and Down
buttons to set the order in which the evaluations will appear in the table.
For each evaluation, a description can be specified under the Description column. Use this to
provide a clear textual description; this will be used in the report to convey what has been
evaluated.
For each condition, select which Matrix the condition applies to. For example, if you plan to
evaluate a quantity using measurement data (i.e., a Raw matrix), select the Raw matrix. Any of
the raw or calculated matrices that contain endpoint data can be referenced in an evaluation.
Also, it is possible to validate non-matrix data (such as transform output variables).
If there are no available matrices, then the (NA) label appears. In this case evaluations
can still be created that do not refer to endpoint matrix data.
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Evaluations are not applicable to XY data matrices. To perform an evaluation on XY
data, first use an XY reduction transform and evaluate the endpoint result.
Expression Configuration
The Expression Configuration pane is used to specify the selected evaluation's expression.
The Expression setting is used to define the expression to evaluate. This might reference a
specific sample group or position on the selected matrix. It might also perform a calculation or
function. Refer to the expression section for a complete description of available expression
functionality.
The Numerical Formatting options are used to specify how the computed value is
presented:
Numerical Formatting
Precision
The number of digits to be displayed for a numerical value. The interpretation
of Precision depends on the Type setting.
Specifies how a numerical value is formatted with regard to precision and
rounding options:
Type
l Significant Figures – The value is reported to the specified number of
significant figures. This accepts Precision values in range 1 through 14.
l Decimal Places – The number of digits to display to the right of the
decimal point. This accepts Precision values in range 0 through 14.
Evaluation Results
Evaluations are computed when the data analysis is performed (after all transform
calculations). To apply any new or modified settings, press the Calculate button and inspect
the report to see the results.
Each evaluated expression results in a single numeric value. If there is an error in evaluating
the expression, then the error message is reported (in red) as the result.
All evaluations are performed (irrespective of whether they are included in the
report). You may configure how and if the Evaluation Tables are reported. In any
case, the results of each evaluation test are listed in the Calculation log.
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Examples
Here are examples of evaluations along with descriptions of their behavior. They apply the
evaluation on the selected data matrix.
Evaluates
Computes
Control1
The average of all Control1 replicates (that are not flagged)
Control1/Control2 The ratio of Control1 and Control2
sd(Control1)
The standard deviation of the Control1 replicates (that are not
flagged)
pcv(Control1)
The %CV of the Control1 replicates (that are not flagged)
Unknown
The average of all replicates of all Unknown samples (that are
not flagged)
median
(Unknown)
The median of all Unknown sample measurement (that are not
flagged)
TOV1.R2
The R2 value of the curve fit as used in the first transform
(referenced using a Transform Output Variable)
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Report Content
Overview
The primary output of the data analysis process is the report. MyAssays Desktop provides
comprehensive functionality to configure the report's:
l content
l presentation
l export
This section describes how to configure the content that is included in the report.
Within the MyAssays Desktop Application, the report can be displayed as Sheet
View or Page View; these settings are available in the Report tab. When getting
started, we suggest using the Sheet View so that your outputs are not confined by
the limitations of a printed page. Use the Page View only if and when the layout of
the printed page is important (for page-based reporting, you can define a reporting
template to apply to your MyAssays Desktop output).
Report Content Definition
The content available for inclusion in the report depends on the configuration of each of the
following settings (these each correspond to a tab in the Assay Properties pane):
l Transforms – Any configured transforms will result in calculated elements (such as
numerical results or charts).
l Matrices – The settings of each matrix (raw measurement data and any calculated) can be
configured. This includes how numerical data is presented (numerical formatting settings),
if it is included, how replicate values are reported, and any optional colored value bars.
l Evaluation – Any configured evaluation settings will result in one or more evaluation
tables, each listing the results of the computed evaluations.
l Validation – Any configured validation settings will result in one or more validation
tables, each listing the results (pass or fail) of the computed validations.
Various report elements are available depending on these settings. For example, if a Standard
Curve Fit transform is configured, then the calculated concentrations will be available for
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inclusion in a Sample Table and/or Matrix Table, along with details of the fit (including
standards chart and equations), each of which can be further configured.
The configuration and ordering of each report element is defined in the Report tab.
How to: View Report Tab
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to open it.
3. Select the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
4. Review and edit the report's elements as required.
Report Element Configuration
The Report tab consists of two panes:
l Reports – This pane manages the report elements and their ordering.
l Report Configuration – This displays the available settings for the selected report
element.
Reports Pane
The Reports pane lists the report elements that will be included in the report. Their ordering
reflects the order in which they will be added to the report.
Use the Add Element button to add a new report element. A list of available report elements
will appear. The selected item will be added to the end of the list.
Use the Remove button to remove the selected report element.
To change the ordering of report elements, select an item and use the Up or Down arrow
buttons on the right of this pane.
Report Configuration Pane
The Report Configuration pane displays the properties of the selected report element. The
actual settings depend on the element; however, there are a number of common elements:
General
Id
Each report element is automatically given a unique identifier (Id). This
identifier is used to identify the report element when configuring custom
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report templates (It is also used for advanced settings such as custom export
and XML configuration).
For general use, it should not be necessary to change the name of
the identifier.
Include
This checkbox can be used to exclude the report element (without deleting the
settings).
Optionally provided title for the report element. When specified, the title
appears immediately before the element.
Title
This property can refer to report macros (such as {PlateNum} and
{AnalyteName}) to automatically refer to the data that it is associated with.
If a title is not specified, then the element is displayed without any title.
Sheet
This setting defines the name of a sheet to insert the contents into. This is only
applicable to sheet-based content such as Excel output or data in the Sheet
View.
Sheet
Name
Each report element can be included on its own separate sheet or elements
can be added together on the same sheet. Where a common sheet name is
used across report elements, the elements are added in the order as defined
in the Reports pane.
This property can refer to report macros (such as {PlateNum} and
{AnalyteName}) to automatically group associated content into a sheet of
the same name.
Note, if Sheet Name is defined for one report element, then the report
content is exported on one sheet with provided Name.
When viewing report content in Sheet View, the sheet names appear in the bottom
left of the Report tab (in the Results tab). If there is only one named sheet, then this
sheet name will not appear.
Report Elements and Multiple Plate or Multiplex Analyses
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If your assay comprises of measurements across multiple plates or measurements for
multiple analytes (multiplex assay), then the report will contain an element for each plate or
analyte.
To help arrange this type of content into an easy-to-navigate report, it is recommended to use
the Sheet Name setting; this can be used to arrange content into separate sheets.
For example, with a multiple plate assay, set the Sheet Name setting to:
Plate {PlateNum}
With this setting applied to a Sample Table element, the results for each plate will appear in a
separate sheet tab named on by the plate number (i.e., Plate 1, Plate 2, Plate 3, etc.).
Report Macros
The following table lists the macros that are available for use in the Title and Sheet Name
properties.
Macro
Expands To
{PlateNum}
1-based plate number
{ContainerNum}
1-based container number (for a multiple plate assay, this is the plate
number; for multiplex, this is the analyte number)
{PlateName}
The plate name (as it appears under the Measurements | Plate IDs)
{AnalyteName}
The analyte name (as it appears under the Measurements | Analytes)
{ContainerName}
For a multiple plate assay, this is the plate name; for multiplex, this is the
analyte name
These macros can be used in conjunction with other text (e.g., Plate {PlateNum}
would be expanded to Plate 1 for the first plate).
Report Elements
To simplify configuration, various report elements are automatically added to the report and
configured with default settings as you add new steps to your analysis:
l The Sample Table is the most widely used report element, so it is included by default in all
configurations. The Sample Table displays the results of each defined sample group (in
accordance with the defined layout).
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l If any Evaluation or Validations are configured, an Evaluation or Validation report element
will be added automatically.
l If any transform generates additional content, a Transform Content element will
automatically be added. For example, the Standard Curve Fit transform produces a
standards chart, concentrations table, and equations; this content is automatically added to
the report as a Transform Content report element.
You will see these elements appear automatically in the Reports pane of the Report tab. You
are free to edit or delete this default content as you require. If analysis steps are removed,
then the associated report element will be automatically removed too.
Beyond the automatically added report elements, you can further add your own elements to
the report:
How to: Add a Report Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the required report element from the Add Element drop-down list in the
Reports pane.
3. Review and edit the element's settings as required.
4. Press the Calculate button to perform calculations with the new settings.
The following table describes each report element in more detail along with any applicable
settings.
Report
Element
Description
Settings
Include Description –
Include the description
column.
Evaluations
Table
The details and results of any configured
evaluations arranged in a table.
Include Expression – Include
the expression column.
Include Header – Include the
table header.
Histogram
The Histogram represents the data by
displaying the average of each sample in
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Matrix – The matrix of data to
plot.
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Report
Element
Description
Settings
vertical columns. Each sample is denoted
by a bar of the color of its sample type.
Error Bars – Use this setting to
convey accurate range of +/data error.
In addition, to indicate the error or
Title – The title for a
uncertainty in a reported measurement or histogram chart.
calculated data, the variability of data can
Types – Which sample types
be represented with optional error bars.
to include in the histogram.
Layout
A graphical representation of the sample
layout.
The results displayed in a matrix-table
structure. This is a table matching the
layout dimensions with the data reported
in each cell corresponding to the
associated position on the matrix.
Matrix Table
For endpoint data, a numerical value is
reported. For XY data, a thumbnail plot is
displayed.
Each raw or calculated matrix can be
marked for inclusion or exclusion in a
matrix table using the Report Contents
section of the Matrices tab.
Size – Small, Medium, or
Large (medium size will fit on
a page-width).
Arrangement – Select
Combined for each cell in the
table to list the values of all
included matrices. Or select
Separate for a separate table
for each included matrix.
Thumb Plot – For XY data,
various options for defining
the inclusion and sizing of
XY thumb plot charts.
Thumb Scale – For XY data,
various options for defining
how XY thumb plot charts are
scaled.
A layout-based graphical representation of Matrix – The matrix of data to
the matrix data.
plot.
Matrix View
For endpoint data, a numerical value will
Size – Small, Medium, or
appear at each position. For XY data, a plot Large (medium size will fit on
will appear at each position.
an A4 page-width).
The color of each position can be colored
using the layout color or with a heatmap
to represent the endpoint value intensity.
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Coloring – Layout or
Heatmap (Heatmap setting
does not apply to XY data).
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Report
Element
Description
Settings
Measurement
Meta Data
A table listing the measurements meta
data (reported as name value pairs). This
corresponds to the data as it appears
under the Measurements | Meta tab.
(No additional settings.)
Any notes associated with the assay run.
Notes
QC Results
Notes can be entered using the
Notes button in the View section
of the Home tab.
Inter-assay quality control results (as
defined by Quality Control settings). This
could include Observation Table, LeveyJennings, or CUSUM charts reporting the
latest results along with historical results.
Exclude if Empty – If this
option is set, then this element
is only included if notes are
entered.
(No additional settings.)
Include Positions – Include
the Positions column to report
the sample positions where
the sample was measured.
A table reporting the results of each
defined sample group (in accordance with Replicate Orientation – How
the defined layout). Each row contains the the replicates appear in the
data for a specific sample group.
table.
Sample Table
For endpoint data, a numerical value is
reported. For XY data, a thumbnail plot is
displayed.
Each raw or calculated matrix can be
marked for inclusion or exclusion in a
sample table using the Report Contents
section of the Matrices tab.
Transform
Various report elements generated as a
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Types – Which sample types
to include in the sample table.
Thumb Plot – For XY data,
various options for defining
the inclusion and sizing of
XY thumb plot charts.
Thumb Scale – For XY data,
various options for defining
how XY thumb plot charts are
scaled.
Content – Use this setting to
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Report
Element
Content
Validations
Table
Description
Settings
consequence of a specific transform.
specify which sub-elements to
include (or exclude) and their
ordering. The available subelements depend on the
associated transform.
For example, a Standard Curve Fit
transform adds a concentration table, a
chart, and equations to the report as
Transform Content report elements.
The details and results of any configured
validations arranged in a table. Each
validation result is reported as a pass or
fail.
Include Description –
Include the description
column.
Include Expression – Include
the expression column.
Selecting the Data to Include in a Sample Table/Matrix Table
The data to include in a Sample Table or Matrix Table can be configured on a matrix-by-matrix
basis. For example, you may want to include only certain matrices in your tables, perhaps
excluding the results of intermediary data analysis steps.
The easiest way to specify which data to include in your Sample Tables or Matrix Tables is to
use the matrix options: Include in Matrix Tables and Include in Sample Tables. These
settings are available for each matrix. To access these settings, select the Matrices tab and
select the matrix to edit. These settings apply to all of the Sample Tables or Matrix Tables in
your report.
For each matrix, the Sample Table Replicates setting can be used to specify how to
report the values for replicates in the Sample Table on a matrix-by-matrix.
Working with Multiple Sample Tables or Multiple Matrix Tables
You may want to include multiple Sample Tables or Matrix Tables in your report with each
reporting different aspects of the data.
When working with multiple tables, you might require control over which data to include in
which Sample Tables or Matrix Tables. The Sample Table IDs and Matrix Table IDs settings
can be used to select which tables each matrix should be included in. The table Ids correspond
to the report element Ids of the corresponding Sample Table or Matrix Id (as defined in the
Report tab).
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Each table could be configured to report data for different sample types using the
Sample Table's Type setting.
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Evaluations Table
How to: Add Evaluations Table Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Evaluations Table element from the Add Element drop-down list in the
Reports pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
Depending on the configuration of the Evaluation settings, the results are arranged in one or
more Evaluations Tables. The number of rows in each table corresponds to the number of
configured Evaluations.
Evaluations Table
While the Results are definitely included, you might prefer to hide other columns by
unchecking corresponding checkboxes.
Include Description Include the description column.
Include Expression Include the expression column.
Include Header
Determines whether to include the table header.
The Evaluation Table is also configured with common report settings that are covered in the
Report Content section.
The Evaluation Table typically consists of three columns. For example,
Description
Expression
Results
Evaluation #1
Unknown
0.826
Evaluation #2
median(Unknown)
0.649
Median of all Unknown
median(Unknown)
0.649
Here, the Description column contains the description if specified (e.g., on the third row in
this example) or Expression#{index} if it is not. The Expression column contains the
evaluated expression and the Results display the computed result of each configured
Evaluation.
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Histogram
A histogram can be use to graphically present the spread of measurements or results. This can
be useful for spotting outliers.
Each histogram can be configured to plot samples of one or more types (e.g.,
Standards, Unknowns, etc.). The values of each sample group is plotted as a coloured bar on
the x-axis. The colour of each sample bar matches its sample type with a label to indicate the
sample group name (or any applied Sample ID).
For example, the following Histogram was generated using a configuration to plot the
Average values of Control, Unknown and Standards with error-bars indicating the standard
deviation and a custom title.
How to: Add Histogram Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Histogram element from the Add Element drop-down list in the Reports
pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
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The Histogram is configured with the common report settings as covered in the Report
Content section plus the Histogram specific settings described below.
General
Matrix
The matrix of data to plot. Select the relevant matrix name from a drop-down
list.
Histogram
Use this setting to select the type of error bars to use (or none).
Error Bars
Error bars can display the Standard Error, %CV, or Standard Deviation of
the (not-flagged) replicates of the group. If the Replicates option is selected
the error bars reflect the minimum and maximum value of the replicates of
the sample.
With the None selection, the error bars are not shown.
Title
The title for a histogram chart.
Types
Which sample types to include in the histogram.
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Layout
How to: Add Layout Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Layout element from the Add Element drop-down list in the Reports pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
As with other Report Elements, you can configure how it appears in the report using common
report settings.
In addition, Layout has a configurable Size setting. You can select Small, Medium, or Large
option (medium size will fit on a page-width). If multiple layouts are defined in the assay
protocol, then all of them will be included in the report.
The Layout report element adds an image of the sample layout used in the assay to the report.
This includes a layout itself and a legend with the sample types that are included in the layout.
I.e.:
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Standard
Blank
Control
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Matrix Table
How to: Add Matrix Table Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Matrix Table element from the Add Element drop-down list in the Reports
pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
Matrix Table lists the results of the performed analysis in a matrix-table structure. Flagged
data is displayed as defined by the associated transform and matrix configuration. Each cell in
the table corresponds to the associated position on the matrix.
The Matrix Table is configured with specific to this report element and with general report
configuration settings.
Matrix Table
The matrix table(s) has 2 configurations that define how the results will be displayed.
Select Combined for each cell in the table to list the values of all included
matrices (per plate/analyte). In this case, the final row of the table will
Arrangement contain a key that explains ordering of the matrices.
Or select Separate for a separate table for each included matrix.
For XY data, there are additional settings for configuring how the thumbnail plots are
displayed in the report.
Thumb Plot
For XY data, various options for defining the inclusion and sizing of XY thumb plot charts.
Include
Thumb
Plots
Defines whether thumb plots are included in the report.
Thumb Plot
This specifies the height to use for all thumbs in the report.
Height
Thumb Plot This specifies the width to use for all thumbs in the report.
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Width
For XY data, various options for defining how XY thumb plot charts are scaled. These can be
fixed values (or determined for each position). The plot lines are plotted within this scan.
Thumb Scale
Specifies whether the maximum X scale of each plot is:
Thumb
Auto Scale
Max X
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Max X value.
l Plot – The scale limit of each thumb plot is automatically determined by
the maximum X data in each plot (i.e., each plot may have a different
maximum X limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the maximum X of all plots data points in the table.
Specifies whether the maximum Y scale of each plot is:
Thumb
Auto Scale
Max Y
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Max Y value;
l Plot – The scale limit of each thumb plot is automatically determined by
the maximum Y data in each plot (i.e., each plot may have a different
maximum Y limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the maximum Y of all plots data points in the table.
Specifies whether the minimum X scale of each plot is:
Thumb
Auto Scale
Min X
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Min X value.
l Plot – The scale limit of each thumb plot is automatically determined by
the minimum X data in each plot (i.e., each plot may have a different
minimum X limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the minimum X of all plots data points in the table.
Thumb
Specifies whether the minimum Y scale of each plot is:
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l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Min Y value.
Auto Scale
Min Y
l Plot – The scale limit of each thumb plot is automatically determined by
the minimum Y data in each plot (i.e., each plot may have a different
minimum Y limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the minimum Y of all plots data points in the table.
Thumb
Scale Max
X
Determines custom maximum X scale for thumb plots if Thumb Auto Scale
Max X is Fixed.
Thumb
Scale Max
Y
Determines custom maximum Y scale for thumb plots if Thumb Auto Scale
Max Y is Fixed.
Thumb
Determines custom minimum X scale for thumb plots if Thumb Auto Scale
Scale Min X Min X is Fixed.
Thumb
Determines custom minimum Y scale for thumb plots if Thumb Auto Scale
Scale Min Y Min Y is Fixed.
For example,
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
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This is how the Matrix Table is reported for an endpoint matrix. In each cell, the value for a
corresponding position is displayed. For XY data, each position would contain a thumbnail
plot.
All matrices could be reported separately (i.e., a Matrix Table for each included matrix) or in a
combined arrangement. For example, here is a snippet of a Matrix Table for the XY Reduction
Average raw and calculated data:
Each cell contains the thumbnail plot for corresponding input XY position and the calculated
numerical result.
Matrix Table with Multiple Plate/Multiplex
If the assay defines multiple plates then matrix tables are included for each of them. Each set
of matrices for each plate is prefixed with the plate name and number (e.g., “Plate 1”), then the
matrices are included for that matrix.
With Combined, there will be one matrix table for each plate.
With Separate, there might be more than one matrix table for each plate.
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Matrix View
How to: Add Matrix View Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Matrix View element from the Add Element drop-down list in the Reports
pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
Matrix View displays the data of a specified matrix, using coloring as either Layout or
Heatmap. All corresponding settings of the associated matrix are applied to the view too.
Flagged positions are indicated and displayed with a cross-through it.
Matrix View includes the following configurable settings:
General
Matrix
The matrix of data to plot. Select the relevant matrix name from a drop-down
list.
Size
Small, Medium, or Large (medium size will fit on an A4 page-width).
Matrix View
Colouring
Layout or Heatmap (Heatmap setting does not apply to XY data).
The Matrix View is also configured with common report settings that are covered in the
Report Content section.
Matrix view can be configured with coloring of:
l Layout – Each position shows the numeric value in endpoint matrices or a thumb in XY
data according to its sample type. Also, the sample type legend is included. E.g.:
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l Heatmap – Each position displays the numeric value with respect to the default or custom
heatmap scale. The scale legend is also included. This setting is relevant for endpoint data
only. E.g.:
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Measurement Meta Data
How to: Add Measurement Meta Data Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Measurement Meta Data element from the Add Element drop-down list in
the Reports pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
The Measurement Meta Data is configured with common report settings that are covered in
the Report Content section.
If there is any meta data associated with your measurement data, then it can also be added to
the generated report. This data is reported in a two-column table with the name of each entry
and its value. For example,
Data Filename
C:\...\Documents\MyAssays\Measurements\ELISA Absorbance.xlsx
Data Main Filename
ELISA Absorbance
Data File Created
7/10/2017
Data File Last Modified
7/10/2017
MEASUREMENT START DATE Tue Jun 13 2017 13:02:29 GMT+0100 (GMT Summer Time)
MEASUREMENT COMMENT
Comment On Multiple Lines
User interface program version
3.1.1.9
Instrument interface version 0
Instrument serial number
0
PROTOCOL NAME
ELISA Absorbance
PROTOCOL CREATOR DATE
FACTORY PROTOCOL
PLATE TYPE
PerkinElmer OptiPlate
PLATE FORMAT
96 wells (8X12)
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Here the meta data table features details about the measurements data file and the details
obtained from the VICTOR plate reader.
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Notes
How to: Add Notes to the Analysis
1. Launch an assay protocol or open a results file.
2. In the MAA Ribbon click on the Notes button of the Home tab's View group.
3. The Notes panel will appear down-most of the screen.
4. Enter or paste any Notes as required.
For a particular assay run you may optionally provide notes to store alongside results in the
report (e.g., preparation notes, Lot ID, etc.). This can be used to reference the peculiarities of
this data analysis.
How to: Add Notes Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Notes element from the Add Element drop-down list in the Reports pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
Entered Notes will appear under Notes: title.
Notes
Notes element includes the following checkbox setting:
Exclude If
Empty
This setting automatically excludes Notes from the results, if it contains no
information.
The Notes element is also configured with common report settings that are covered in the
Report Content section.
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Sample Table
How to: Add Sample Table Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Sample Table element from the Add Element drop-down list in the Reports
pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
Sample Table lists the results of the performed data analysis by sample groups. Flagged data
is displayed as defined by the associated transform and matrix configuration. Each row in the
table corresponds to a sample.
The Sample Table is configured with general report and specific to this element settings. The
former are described in general report configuration settings and the latter are covered below.
Sample Table
Include Pos- Include the Positions column to report the sample positions where the sample
itions
was measured.
How the replicates appear in the table:
Replicate
Orientation
l Vertical – Each position is listed in a new line of the column.
l Horizontal – All positions are listed in the same line.
Types
Which sample types to include in the sample table.
For XY data, there are additional settings for configuring how the thumbnail plots are
displayed in the report.
Thumb Plot
For XY data, various options for defining the inclusion and sizing of XY thumb plot charts.
Include
Thumb
Plots
Defines whether thumb plots are included in the report.
Thumb Plot This specifies the height to use for all thumbs in the report.
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Height
Thumb Plot
This specifies the width to use for all thumbs in the report.
Width
For XY data, various options for defining how XY thumb plot charts are scaled. These can be
fixed values (or determined for each position). The plot lines are plotted within this scan.
Thumb Scale
Specifies whether the maximum X scale of each plot is:
Thumb
Auto Scale
Max X
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Max X value.
l Plot – The scale limit of each thumb plot is automatically determined by
the maximum X data in each plot (i.e., each plot may have a different
maximum X limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the maximum X of all plots data points in the table.
Specifies whether the maximum Y scale of each plot is:
Thumb
Auto Scale
Max Y
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Max Y value;
l Plot – The scale limit of each thumb plot is automatically determined by
the maximum Y data in each plot (i.e., each plot may have a different
maximum Y limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the maximum Y of all plots data points in the table.
Specifies whether the minimum X scale of each plot is:
Thumb
Auto Scale
Min X
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Min X value.
l Plot – The scale limit of each thumb plot is automatically determined by
the minimum X data in each plot (i.e., each plot may have a different
minimum X limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
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determined from the minimum X of all plots data points in the table.
Specifies whether the minimum Y scale of each plot is:
Thumb
Auto Scale
Min Y
l Fixed – The scale limit of the thumb plots used in the table is specified by
the associated Thumb Scale Min Y value.
l Plot – The scale limit of each thumb plot is automatically determined by
the minimum Y data in each plot (i.e., each plot may have a different
minimum Y limit).
l Table – The scale limit of every thumb plot in the table is the same. This is
determined from the minimum Y of all plots data points in the table.
Thumb
Scale Max
X
Determines custom maximum X scale for thumb plots if Thumb Auto Scale
Max X is Fixed.
Thumb
Scale Max
Y
Determines custom maximum Y scale for thumb plots if Thumb Auto Scale
Max Y is Fixed.
Thumb
Determines custom minimum X scale for thumb plots if Thumb Auto Scale
Scale Min X Min X is Fixed.
Thumb
Determines custom minimum Y scale for thumb plots if Thumb Auto Scale
Scale Min Y Min Y is Fixed.
Here is an example of a Sample Table:
It includes snippet of five Standard samples and the positions they are designated to. Also, the
raw values (My Raw Data column) for each replicate and the calculated results per sample
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for the Percentage Coefficient of Variation transform (%CV column) are included. Each row
contains replicates of the corresponding sample in the Vertical orientation.
For XY data, the Sample Table contains Thumb Plots that is simply a thumbnail graphical
representation of each kinetic/spectral data plot at a position.
Here the replicates for each sample group, XY Raw data and calculated results for XY
Replicates Average transform are displayed in Horizontal orientation.
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Transform Content
Some transforms can output additional items in the report. If the transform applied to the
assay possesses any kind of optional items, they are added to the report automatically. Certain
transforms comprise a number of optional content.
The additional report elements are:
l Additional Sample Table Column – This is an optional additional column that can be
included in the sample table. For example, the Dilution Factors transform outputs optional
report content named Factor Column.
l Chart – The visual representation of a plotted curve that is included in a report by default.
E.g., here is a chart for Standard Curve Fit transform example.
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l Table – The table containing additional values computed from a transform. For example,
this might refer to Goodness Measures Table, Dilutions Table, Curve Data Table, etc.
l Equation – The formula of a fit method that was used to plot a curve. E.g., the formula for
Linear Regression:
A report can contain one or more Transform Content sub-elements.
How to: Configure Transform Content in the Report
1. Open the Report tab.
2. Select Transform Content element, which contains transform name in the title, e.g.
TransformContentDilutionCurves1.
3. Click on a drop-down of Content option of the Report Configuration pane.
4. Review and edit content items as required.
5. Press Calculate button to perform calculations with the new settings.
The Histogram is configured with common report settings that are covered in the Report
Content section and the setting described below.
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Content
Use this setting to specify which sub-elements to include (or exclude) and
their ordering. The available sub-elements depend on the associated
transform.
You can configure which optional items are actually included in report with the Content
Editor. Select the sub-element, move in to the Included/Excluded pane with right/left arrow
buttons, or change its' positioning with the Up and Down buttons on the right of the Included
pane.
To include or exclude all transform content from the report, use the Add All or Remove All
buttons, respectively.
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Validation Table
How to: Add Validations Table Element to the Report
1. Open the Report tab (If the Report tab is not immediately visible, press the << Show
All Properties button to expand).
2. Select the Validations Table element from the Add Element drop-down list in the
Reports pane.
3. Review and edit the settings as required.
4. Press the Calculate button to perform calculations with the new settings.
Validations Table indicates whether each validation has passed or failed and, when Validations
are defined, is added to the report automatically.
By default Validation table in Report is displayed as three-column table with the number of
rows corresponding to the number of Conditions added. The columns are Results,
Expression and Description.
While the Results are definitely included to the report, you might prefer to hide former
properties by unticking corresponding checkboxes.
Include
When checked, the column contains Description if specified or Condition#
Description {index} if it was not.
Include
Expression
If included, this outputs the Condition defined for each Validation.
The Validation Table is also configured with common report settings that are covered in the
Report Content section.
The Validation Table typically consists of three columns. For example,
Results
Expression Description
Fail
x < 2.5
All non-flagged positions have a value less than 2.5 (Position type)
Pass
x < 2.5
The average of the replicates (that are not flagged) of all
sample groups is less than 2.5 (Group type)
Pass
Unknown1
Condition #3
> 1.5
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Results
Syntax Error
Expression Description
Control1 !<
Condition #4
1.5
Here, the Results column displays the evaluated result of each configured Validation. For the
Fail and Inconclusive (i.e., Syntax Error), the row is highlighted with yellow and red
background color, respectively. The Expression column contains the verified condition. Note,
that the expression for the first and the second validation is the same, but the type of
validations is different, as stated in the Description. Finally, the Description column contains
the description if specified (e.g., on third row) or Condition#{index} if it is not.
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Report Template
Overview
The report template enables presenting your analysis results tailored to your laboratory's
standards and requirements. This feature merges together report elements with a
preconfigured template. Full power of MS Word or WordPad features is available to customise
page-based reporting of the results.
For example, you can define custom sequence and/or inclusion of report elements, insert
relevant images or textual descriptions for a printed document. Moreover, simply apply
styling settings and include the results you require for your current purpose.
Report template affects Page View and page-based outputs (DOC, DOCX, PDF) of the
analysis results only.
Report Template Configuration
How to: Add New Report Template for Page-Based Reporting
1. Launch an assay protocol or open a results file.
2. Select the Ribbon's Report tab and switch to the Page View of the results (If the
Report tab is not immediately visible press the << Show All Properties button to
expand).
3. Hover to Template group and press New button. This launches the Template.DOCX file
with default application (typically MS Word or WordPad).
4. Review and edit the reporting template content as required.
5. After making and saving changes to the report template press Calculate to generate a
new report with the new settings.
The application displays a Template.DOCX file for editing – this is the template file. Initially, it
contains the MyAssays report element identifiers (Ids) in curly braces.
Report templates have no restrictions with regard to the specific information they contain.
You can make any changes to the template and save them. For example, reposition, remove or
duplicate Ids, change their font/style/etc., add textual information, macros etc. In fact, any
change is possible provided it is supported in the editor.
How to: Add Report Element Id to the Report Template
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1. Launch an assay protocol or open a results file.
2. Open Report tab.
3. Select the Report Element from the list.
4. Copy the element's Id and insert it in the appropriate place of the template in curly
brackets (e.g., {SampleTable1}).
5. Save changes to the report template and press Calculate to generate a new report with
the new settings.
When you have made changes to the template file and saved them, you can switch back to
MAA and press Calculate to generate a new report with the new settings while keeping the
editor open. This way you can review modified report in Page View and make further changes
if needed.
The defined template will be applied for all page-based reports for this assay results.
Only one template can be defined for the assay results file at a time. If the previously
defined template is no longer useful, edit or delete it.
Edit/Remove Existing Template
After you have created New template file, the Template group expands with two new
options:
l Press Edit button to modify you template file's content according to freshly added report
elements or updated requirements. It will launch your default .DOCX editor with the
existing template file.
l Press Delete button to remove the existing template file (a warning message will prompt
for confirmation before this action is completed).
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Reference
Transforms
Transforms can be described as a set of configurable operations that are performed to
generate results. The results might be included in a report or used as inputs to other
transforms. You can add multiple transforms in any order to perform a required data analysis
with the leverage of transforms performed previously.
Most transforms use one input matrix and produce one output matrix (like Factor). However,
this is not always the case. Here are the general rules:
l Each transform can have one or more input matrices.
l Each transform can produce one or more output matrices.
l A particular transform will always have the same number of input and output matrices.
l The input and output matrix types of a transform are fixed.
Matrices and Transforms
Input Matrices
A transform can accept one or more input matrices. Where an input matrix is used, the
transform performs calculations using the data on those matrices. For example, the Factor
transform multiplies all points on the input matrix by a specific value.
The input matrices available to a transform include the compatible raw data (i.e., of a
particular data type) and compatible data that has been calculated prior to this transform.
For example, assuming all data is endpoint, the first transform can only access data on the
Raw matrices, whereas a second transform can access data on the Raw matrices and/or the
first Calculated matrix.
Output Matrices
Each transform will add one or more calculated matrices to the Matrices at each step. As each
transform is evaluated, each container in the results will always contain the same number of
matrices (raw and calculated) as all other containers in the results.
As you add transforms, you can configure output settings and display in the report for each of
them.
Expressions and Transforms
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In addition to the input matrices, any transform that uses MyAssays Analysis Expressions can
access data directly from the current transform (including data that is not on the input
matrices). Similar to available input matrices, the data that can be accessed depends on the
data that has been calculated up to this point of the evaluation (and data of the compatible
type). Thus, the data accessible by an expression includes compatible raw and calculated
matrices.
Transform Manager
Transforms are added and configured via the Transform Manager. The Transform Manager
occupies the full width and height available for the transform tab’s contents.
How to: Add the Transform
1. Launch an assay protocol or open a results file.
2. If you cannot see the Assay Properties panel, press the Properties button to open it.
3. Press the Add button.
4. Select the transform.
5. Press the Create button.
6. Review or edit the transform's input matrix and output matrix name as required.
7. Provide your required settings for the transform (see Properties section above).
8. Press the Calculate button to perform the calculations with the new settings and to see
the results.
Each transform has a set of configurations to specify the parameters of the analysis. Some
transforms can also add optional content to the report and be configured with specific
calculation settings.
Transform Relevance
A transform may or may not be relevant to the current assay configuration. In this context, the
configuration refers to:
• Matrices (both raw and calculated) – Certain transforms are required to be executed for
a particular data type.
• Layout – Some transforms require specific layout rules to be followed.
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General Transform Properties
With regard to flexibility of assay setup, each transform can be configured to fully match assay
requirements with a number of settings.
Each transform has zero or more specific settings that allow customization of the transform
operations. These are covered under each transform section specifically.
On top of that, all transforms share the following common settings:
Advanced
Included
Samples
The sample types to be computed by this transform.
Sample types not selected in the Included Samples option will not be
computed by this transform; for those cases, this setting specifies what the
result will be for those excluded samples:
Excluded
Results
l Flagged With Value – The result is flagged with the input value reported.
This will appear in the report with the value with strikethrough, e.g., 1.23.
l Flagged No Value – The result is flagged with no value reported. This
will appear in the report as Flagged.
l Equal Input – The result is set to match the value of the input.
Units
The measurement units to be included in the header of the Sample Table.
Output
Units
If the output units of the transform are the same as the input, then use "ditto"
units to automatically carry these units through (i.e., apply the same units to
the transform’s output). This shortcut means that it is not necessary to repeat
settings across transforms. Ditto units are specified with two dots, i.e.:
..
In addition, the XY transforms and XY Reduction transforms can be configured with Data
and Range settings.
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Endpoint Transforms
Endpoint transforms operate on matrices of data that consist of a single numerical value at
each position on the layout. An endpoint transform will compute a single numerical result for
each position on the layout.
For example, with a typical ELISA microplate assay, a plate-reader will measure and report a
single numerical value for each successfully measured well on the plate. Each measurement
value is the endpoint of the detection technology for the observed sample. The Standard
Curve Fit transform is an endpoint transform that can compute the concentration endpoint
from the measurement endpoint.
For consistency, we refer to all matrices with a single numerical result as an
endpoint matrix. Strictly speaking, of course an endpoint matrix might not actually
be the final conclusion of the analysis. With MyAssays Desktop, you can layer as
many transforms as you require to perform the various analysis steps that are
required (resulting in multiple "intermediary endpoint" matrices).
Flagged Endpoints
All transforms take into account the flagged status of each input value. Any data point that is
flagged is not included in any calculation and ignored in any reference to the sample group
that it belongs to.
For example, if a transform subtracts the average of a blank group from all samples, if the
blank group is made up of 3 replicates but the 3rd replicate is flagged, then all the samples
will be corrected by the average of the 2 not-flagged samples.
In addition, a transform computation for a position might result in a flagged endpoint result.
This could be because of a calculation error or because the transform has performed some
kind of automatic outlier detection (such Auto Flag and Replicate Outlier Removal).
Sample Types to Process
For all endpoint transforms, by default the calculations are performed on all sample types.
For greater flexibility, the Advanced settings of each transform can be used to select to
perform the transform's computations on only a subset of the sample types. In this case, it is
possible to set what the excluded samples results should be (e.g., flagged or to equal the input
endpoint).
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For example, it might only make sense to perform a certain calculation on Unknown and
Control samples but not with other sample types.
Endpoint Transforms
Transform
Operation
Auto Flag
Flag all data points that satisfy the specified Boolean expression.
Average
Calculate the average of replicates for each sample group.
Blank
Correction
Subtract the specified blank value from each input data point.
Dilution
Curves
Create a dilutions chart for each group of the specified sample type, fit selected curve-fitting method, and calculate the specified titer from the curve.
Dilution
Factors
Multiply sample groups of the selected type(s) by a specified factor (a unique
value can be specified for each group).
Expression
by Matrix
(Dual)
Evaluate the expression in terms of x and y for each sample group. The x and
y variables refer to the values on the first and on the second input matrix,
respectively.
Expression
by Matrix
Evaluate the expression in terms of x.
Expression
by Position
Evaluates an expression in terms of x for each specified position.
Factor
Multiply all input data points of the specified sample type(s) by a specified
factor.
Matrix
Difference
Calculate the difference between values on the first matrix and the second for
each sample group.
Median
Calculate the median of the replicates for each sample group.
Percentage
Coefficient of Calculate the %CV of the replicates for each sample group.
Variation
Percentage
Calculates a percentage value for each data point with respect to the specified
0% and 100%.
Replicate
Outlier
Removal
Flags the specified number of replicates for each sample group, in which %CV
of its replicates is greater than specified threshold.
Standard
Curve Fit
Constructs a standard curve by plotting the standard input concentration
values vs data points.
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Transform
Operation
Standard
Deviation
Calculate the standard deviation of the replicates for each sample group.
Standard
Error of
Mean
Calculate the standard error of the mean for replicates of each sample group.
Variance
Calculate the variance of the replicates for each sample group.
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Auto Flag
[Input: Endpoint
Output: Endpoint]
This transform flags positions where a Boolean condition is satisfied. This is useful for
automatically rejecting and ignoring data points that are outside an expected range.
Properties
Settings
The expression to evaluate, where a single x variable is set to value of each
position of the input matrix
Auto Flag
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example, we would like to flag all positions with a value greater than 1.
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
To flag values in this matrix where the measurement is greater than 1, specify an Auto Flag
expression of x > 1.
Calculated
The transform will generate an output matrix named Calculated with the value at each
position matching the input data and positions that do not meet the specified condition
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flagged. With the example data provided above, the results are as follows (also displayed here
to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
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Average
[Input: Endpoint
Output: Endpoint]
This transform calculates the average of the replicates in each sample group (flagged
replicates are not included).
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find the average position values in each
sample group.
This transform averages the replicates of the sample group and stores the resulting
value to each sample group replicate of the output matrix. Therefore, the result will
depend on the layout configuration.
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
The example uses a layout with Unknowns in duplicate (e.g., Unknown1 at A1 and A2,
Unknown2 at B1 and B2 etc.). I.e.:
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Unknown
Average
The transform will generate an output matrix named Average with the value at each position
being the averaged input data for each sample group. With the example data provided above,
the results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.344 0.344 2.115 2.115 1.220 1.220 1.175 1.175 0.113 0.113 1.472 1.472
B 0.448 0.448 1.879 1.879 1.211 1.211 1.193 1.193 0.131 0.131 0.777 0.777
C 0.581 0.581 0.494 0.494 1.223 1.223 1.207 1.207 0.115 0.115 0.620 0.620
D 0.790 0.790 0.371 0.371 1.177 1.177 1.226 1.226 0.104 0.104 0.490 0.490
E 1.085 1.085 0.323 0.323 1.220 1.220 1.248 1.248 0.113 0.113 1.564 1.564
F 1.435 1.435 0.395 0.395 1.201 1.201 1.213 1.213 0.109 0.109 0.784 0.784
G 1.797 1.797 0.579 0.579 1.217 1.217 1.307 1.307 0.127 0.127 0.656 0.656
H 0.262 0.262 0.467 0.467 1.238 1.238 0.116 0.116 0.115 0.115 0.484 0.484
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Blank Correction
[Input: Endpoint
Output: Endpoint]
This transform subtracts the average of a specified blank group (or groups) from each sample.
This is useful for subtracting background noise from measurements.
The blank value is computed from the average of the (not-flagged) blank replicates.
In the simplest configuration, a single blank value is subtracted from all samples. More
sophisticated correction methods are possible, including:
l 1-1 – Where each sample group is corrected by its associated blank group
l by-row – One blank group on each row
l by-column – One blank group on each column
For more complicated blank correction calculations, an alternative approach is to use
the Expression by Position transform.
Properties
Settings
Blank
Group
The group number of the sample to use for correction. This can be defined as
a number or as an expression. The expression can refer to x, which is the
group number of the sample to be corrected. For example, set this to x to
perform a 1-to-1 correction where each group is corrected by the sample of
the specified blank type with the same group number.
Blank Type The sample type used as the blank.
Example: Single Blank
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to perform a blank correction using the average
of one blank group. This blank group itself is measured in duplicate. The mean of these
replicates will be subtracted from all other samples.
Here is some example raw data displayed to 3 decimal places:
Raw
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1
2
3
4
5
6
7
8
9
10
11
12
A 0.192 0.199 0.347 0.346 0.325 0.321 0.803 0.797 1.589 1.593 0.241 0.246
B 0.293 0.300 0.586 0.584 0.800 0.793 0.402 0.401 0.839 0.837 0.562 0.564
C 0.537 0.534 0.549 0.547 0.878 0.875 0.471 0.471 0.759 0.756 1.039 1.043
D 0.858 0.855 0.320 0.325 0.531 0.530 1.552 1.551 0.429 0.427 0.424 0.424
E 1.523 1.527 0.486 0.490 1.289 1.283 1.430 1.438 1.494 1.501 1.427 1.429
F 1.622 1.621 0.621 0.626 0.667 0.663 1.350 1.347 0.631 0.634 1.695 1.694
G 0.001 0.001 0.356 0.352 0.418 0.412 1.172 1.174 1.572 1.570 0.595 0.590
H 0.297 0.297 0.796 0.801 1.517 1.515 0.960 0.965 0.534 0.527 0.585 0.584
This transform requires a layout with a minimum 1 Blank group.
Layout
The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
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Standard
Blank
Control
Unknown
To subtract the mean of the blank positions from the measurements, specify Blank Group to
1 and Blank Type to Blank.
Blank Corrected
The transform will generate an output matrix named Blank Corrected with the value at each
position being the subtraction of the blank value from the input data. With the example data
provided above, the results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.191 0.198 0.346 0.345 0.324 0.320 0.802 0.796 1.588 1.592 0.240 0.245
B 0.292 0.299 0.585 0.583 0.799 0.792 0.401 0.400 0.838 0.836 0.561 0.563
C 0.536 0.533 0.548 0.546 0.877 0.874 0.470 0.470 0.758 0.755 1.038 1.042
D 0.857 0.854 0.319 0.324 0.530 0.529 1.551 1.550 0.428 0.426 0.423 0.423
E 1.522 1.526 0.485 0.489 1.288 1.282 1.429 1.437 1.493 1.500 1.426 1.428
F 1.621 1.620 0.620 0.625 0.666 0.662 1.349 1.346 0.630 0.633 1.694 1.693
G 0.000 0.000 0.355 0.351 0.417 0.411 1.171 1.173 1.571 1.569 0.594 0.589
H 0.296 0.296 0.795 0.800 1.516 1.514 0.959 0.964 0.533 0.526 0.584 0.583
Example: 1-1 Blank Correction
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example, we would like to perform a 1-to-1 correction, where each
sample group is corrected by the blank that it is associated with. The blank numbering used in
the layout defines the associations.
Here is some example raw data displayed to 3 decimal places:
Raw
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1
2
3
4
5
6
7
8
9
10
11
12
A 1.962 1.999 2.038 1.922 1.995 0.002 1.628 1.670 1.624 1.554 1.701 0.000
B 0.784 0.760 0.754 0.751 0.797 0.002 1.124 1.104 1.072 1.077 1.106 0.002
C 0.586 0.575 0.582 0.590 0.576 0.001 0.724 0.737 0.690 0.711 0.717 0.002
D 0.441 0.441 0.427 0.419 0.439 0.002 0.095 0.095 0.093 0.096 0.096 0.001
E 1.956 1.970 1.875 2.043 1.959 0.001 1.449 1.479 1.428 1.395 1.385 0.001
F 1.416 1.358 1.407 1.352 1.452 0.001 0.242 0.230 0.249 0.235 0.251 0.000
G 1.438 1.429 1.488 1.401 1.372 0.002 0.943 0.952 0.956 0.912 0.969 0.002
H 0.840 0.827 0.812 0.838 0.860 0.002 1.320 1.260 1.313 1.363 1.312 0.001
Layout
The example uses a layout with 16 Unknowns groups (with Unknown1 in positions from A1
to A5, Unknown2 in positions from B1 to B5, etc.) and 16 Blank singlets I.e.:
To perform 1-1 correction, specify Blank Group to x and Blank Type to Blank.
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With this specified, the value of each blank will be subtracted from every position of
associated group. In other words, Blank1 (stored in A6) value will be subtracted from A1, A2,
A3, A4, A5 (the members of Unknown1), and from A6 itself. Therefore, the calculated matrix
will contain blank corrected Unknown values and all Blanks will be 0.
Blank Corrected
The transform will generate an output matrix named Blank Corrected with the value at each
position being the subtraction of the associated blank value from the input data. With the
example data provided above, the results are as follows (also displayed here to 3 decimal
places):
1
2
3
4
5
6
7
8
9
10
11
12
A 1.960 1.997 2.035 1.919 1.992 0.000 1.628 1.670 1.623 1.554 1.701 0.000
B 0.783 0.758 0.752 0.750 0.796 0.000 1.122 1.102 1.070 1.075 1.104 0.000
C 0.585 0.574 0.581 0.589 0.575 0.000 0.722 0.735 0.688 0.709 0.714 0.000
D 0.439 0.439 0.425 0.417 0.437 0.000 0.094 0.094 0.093 0.095 0.095 0.000
E 1.955 1.969 1.873 2.042 1.958 0.000 1.448 1.478 1.427 1.394 1.384 0.000
F 1.415 1.358 1.406 1.352 1.451 0.000 0.241 0.229 0.249 0.235 0.251 0.000
G 1.436 1.427 1.486 1.399 1.369 0.000 0.941 0.950 0.955 0.910 0.967 0.000
H 0.839 0.825 0.810 0.837 0.858 0.000 1.319 1.260 1.312 1.362 1.311 0.000
Advanced Example: By-Row Blank Correction
In this example we would like to configure blank correction on a row-by-row basis to blank
correct each sample by the average of the two blank samples of the same row.
For better clearness of the process, this example uses simplified endpoint data for a 12x8
microplate:
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1
2
3
4
5
6
7
8
9
10 11 12
A 1
1
2
3
4
5
6
7
8
9
10
1
B 2
11
12
13
14
15
16
17
18
19
20
2
C 3
21
22
23
24
25
26
27
28
29
30
3
D 4
31
32
33
34
35
36
37
38
39
40
4
E 5
41
42
43
44
45
46
47
48
49
50
5
F 6
51
52
53
54
55
56
57
58
59
60
6
G 7
61
62
63
64
65
66
67
68
69
70
7
H 8
71
72
73
74
75
76
77
78
79
80
8
Layout
The example uses a layout with 80 singlet Unknowns and 8 Blank duplicates for each row
(with Blank1 in positions A1 and A12, Blank2 in positions B1 and B12, etc.) I.e.:
Blank
Unknown
With the Blank Group setting defined as the expression:
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1 + floor ((x -1) / (LayoutWidth()-2),1)
This converts the group number of the sample to blank correct (x) to the blank number on its
row. In other words, for x=1, this means 1+floor((1-1)/10),1), floor=0 and the Blank Group
is taken as 1. For the first row, x is in range from 1 to 10 and the expression outputs 1, thus
for Unknowns from 1 to 10 the average of Blank1 is subtracted.
For the second, x is from 11 to 20, floor=1, so the Blank2 group is subtracted from
Unknowns11-20, etc.
Blank Corrected
1
2
3
4
5
6
7
8
9
10 11 12
A 0
0
1
2
3
4
5
6
7
8
9
0
B 1
9
10
11
12
13
14
15
16
17
18
1
C 2
18
19
20
21
22
23
24
25
26
27
2
D 3
27
28
29
30
31
32
33
34
35
36
3
E 4
36
37
38
39
40
41
42
43
44
45
4
F 5
45
46
47
48
49
50
51
52
53
54
5
G 6
54
55
56
57
58
59
60
61
62
63
6
H 7
63
64
65
66
67
68
69
70
71
72
7
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Dilution Curves
[Input: Endpoint
Output: Endpoint]
This transform creates a curve fit and calculates, in respect of this curve, a titer value for each
sample group of the specified dilution type.
Properties
Settings
Dilution
Type
Dilutions
The sample type to use for the dilution.
This specifies standard concentration values. You can Enter or Paste the
values into the edit box. If the values are in a sequence, select the Series
button to define the series. The series is calculated from the value specified in
the first row and the sequence settings with arithmetic operators and a factor
(either even or fractional). Also, you can press the r button to set a number to
repeat sequence with in the edit box.
All sample groups are assumed to have the same number of
positions defined on the layout (e.g., if Unknown1 has 10 samples,
then all Unknowns on the layout must also have 10 samples).
Fit Method
The curve fitting method to construct a curve for your data based on the
specified Dilution Type.
Titer To
Calculate
An expression representing the titer value to calculate.
Weight
Method
Specifies the weight function for the transform.
A type to plot an X axis in a chart:
X Axis Type l Logarithmic
l Linear
X
Transform
Function for X values transform.
Y Axis Type A type to plot an Y axis in a chart:
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l Logarithmic
l Linear
Y
Transform
Function for Y values transform.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to create a curve fit for each sample group of
the Unknown type.
Here is some example raw data displayed here to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.000 2.682 2.345 2.612 2.732 2.675 2.664 2.574 2.580 2.361 0.871 2.839
B 0.000 2.577 1.735 2.518 2.701 2.537 2.571 2.450 2.453 2.171 0.437 2.809
C 0.000 2.537 1.290 2.497 2.683 2.447 2.490 2.273 2.344 1.695 0.222 2.783
D 0.000 2.358 0.793 2.318 2.585 2.195 2.205 1.856 2.069 1.070 0.094 2.658
E 0.000 1.958 0.369 1.799 2.224 1.636 1.661 1.192 1.485 0.550 0.037 2.322
F 0.000 1.352 0.151 1.118 1.517 0.909 0.947 0.638 0.868 0.257 0.013 1.495
G 0.000 0.745 0.053 0.540 0.872 0.425 0.485 0.282 0.417 0.094 0.002 0.862
H 0.000 0.312 0.018 0.250 0.398 0.180 0.200 0.113 0.194 0.038 0.001 0.438
Layout
The example uses a layout with one Unused column (in positions from A1 to H1) and
Unknowns in columns (e.g. Unknown1 at positions from A2 downward to H2, Unknown2
at A3-H3, etc.). I.e.:
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Unused
Unknown
To create a curve and calculate concentrations for this matrix, specify following settings:
1. Dilution Type to Unknown.
2. Dilutions count is equal to maximum number (8) of replicates in sample group of the
Unknown. Set this parameter to 0.01 and series to divide by 3.
3. Set Fit Method of 4PL.
4. Set a Titer To Calculate to 1.5.
5. Select a Weight Method of None.
6. Set an X Axis Type to Logarithmic.
7. Set a X Transform to None.
8. Set an X Axis Type to Linear.
9. Set a X Transform to None.
The transform generates a Dilutions Table and a Chart by default. Other elements such as
Matrix table and Notes can be added to report optionally.
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Dilutions Table
This table lists coefficients calculated from the selected Fit Method and a resulting value for
each sample. With the example data provided above, the results are as follows:
Sample
c
b
Unknown1
-0.0584816
0.875198 0.0000376679 2.67099 0.999497 0.0000522186
Unknown2
-0.0901615
0.606878 0.00226574
Unknown3
0.103291
1.07281
0.0000588689 2.59484 0.999371 0.0000738691
Unknown4
0.176798
1.10415
0.000035979
Unknown5
0.0355624
0.998377 0.0000794665 2.64392 0.999147 0.000101774
Unknown6
0.0581275
0.980399 0.000077654
2.66458 0.999619 0.0000965551
Unknown7
0.0190159
0.929062 0.000146444
2.60957 0.999764 0.000199822
Unknown8
0.0468791
0.936045 0.000090142
2.57555 0.999425 0.000124316
Unknown9
0.0138517
0.919968 0.000515222
2.53025 0.999769 0.000767282
Unknown10 -0.00938083 0.681115 0.135122
Unknown11 0.298789
1.20751
d
R2
a
Result
3.29931 0.997736 0.00184835
2.73875 0.999281 0.0000381935
6.05263 0.999738 0.0267169
0.0000424681 2.83485 0.999004 0.0000389155
Chart
The chart contains a curve fit plotted with 4PL for titer and for each sample group.
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Titer
The output matrix named Titer can be optionally added to the report. It contains the
calculated titer value for each data point. With the example data provided above, the results
are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
B Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
C Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
D Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
E Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
F Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
G Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
H Flagged 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.027 0.000
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Dilution Factors
[Input: Endpoint
Output: Endpoint]
This transform multiplies each selected sample group by a specified factor (a factor can be
specified for each sample group).
A sample prepared with a dilution of 1:2 would use a dilution factor of 2.
Properties
Settings
Factored
Samples
The sample types to which the dilution factor should be applied
Factors
The dilution factor to use for each selected Factored Samples
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to multiply all odd Unknown sample groups
by 2 and all even Unknown sample groups by 5.
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
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The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
Standard
Blank
Control
Unknown
To multiply selected sample types in this matrix by specified factors, set Factored Samples to
Unknown and specify Factors as 2 for every odd sample group and as 5 for every even
sample group.
Concentration (x Dil. Factor)
The transform will generate an output matrix named Concentration (x Dil. Factor) with the
value at each position of the selected Factored Samples multiplied by specified Factor and
sample groups of other types matching the input data.
With the example data provided above, the results are as follows (also displayed to 3 decimal
places):
l Sample Table
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Here is a Sample Table snippet, which lists the details of the calculations for Unknown samples
and the additional report content. For this transform, it is a Factor column displaying specified
Factor for each sample group.
For each sample, the following details are reported:
The location of Factor column in the Sample table can be customized in the XML tab.
How to: Set Factor Column Position in the Report
1. Select the XML tab in the Assay Properties panel (If the XML tab is not immediately
visible, press the << Show All Properties button to expand).
2. Inside the ReportContent element, locate the Factor element.
3. Add the SampleTableColumnPosition element to the Factor element. Specify the
column positioning with one of the following options:
l AfterOutputMatrix – On the right of the output matrix
l LeftMost – Before all matrix data
l RightMost – After all matrix data
l Matrix Table
Here all calculated data is displayed in a Matrix Table.
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1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 4.210 4.248 2.426 2.452 2.318 2.380 0.220 0.232 2.926 2.962
B 0.455 0.441 9.345 9.440 6.015 6.090 5.900 6.030 0.660 0.650 3.930 3.840
C 0.587 0.575 0.948 1.028 2.412 2.478 2.406 2.420 0.226 0.232 1.226 1.252
D 0.806 0.774 1.820 1.890 5.835 5.935 6.055 6.205 0.505 0.530 2.445 2.450
E 1.105 1.065 0.558 0.734 2.430 2.450 2.466 2.524 0.222 0.228 3.158 3.098
F 1.425 1.445 1.890 2.055 5.980 6.030 6.130 5.995 0.545 0.540 3.960 3.875
G 1.791 1.803 1.120 1.194 2.410 2.456 2.616 2.612 0.252 0.254 1.304 1.318
H 0.253 0.270 2.315 2.350 6.070 6.305 0.575 0.580 0.575 0.575 2.450 2.385
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Expression by Matrix (Dual)
Input: Dual Endpoint
Output: Endpoint
This transform evaluates an expression defined in terms of x and y for all data points
(excluding flagged samples). x refers to the data for the sample on the first input matrix, and y
refers to the data for the same sample on the second input matrix.
Properties
Settings
Dual Matrix The expression to evaluate, where x and y variables are set to value of each
Expression position of the input matrices
If the expression does not contain the x and y variables, the transform will output the
same evaluation result for all positions. For example, if you enter {2^3+4}, the
resulting matrix will contain 12 in each position.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw (1) and Raw (2) matrices. In this example we would like to summarize position
values in these matrices.
Here is some example raw data displayed to 3 decimal places:
Raw (1)
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
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Raw (2)
1
2
3
4
5
6
7
8
9
10
11
12
A 0.204 0.201 0.213 0.209 0.206 0.208 0.216 0.217 0.216 0.213 0.205 0.218
B 0.216 0.200 0.214 0.214 0.204 0.201 0.215 0.216 0.203 0.219 0.207 0.204
C 0.206 0.210 0.208 0.203 0.201 0.218 0.212 0.217 0.216 0.213 0.208 0.204
D 0.215 0.215 0.214 0.203 0.214 0.213 0.219 0.219 0.213 0.201 0.202 0.219
E 0.215 0.220 0.219 0.212 0.210 0.211 0.214 0.214 0.201 0.202 0.202 0.207
F 0.214 0.212 0.202 0.200 0.201 0.217 0.213 0.209 0.218 0.207 0.216 0.216
G 0.211 0.211 0.218 0.209 0.214 0.213 0.201 0.211 0.205 0.210 0.201 0.212
H 0.216 0.209 0.215 0.206 0.218 0.206 0.209 0.219 0.215 0.219 0.216 0.217
To add values from Raw(2) to corresponding values of Raw(1), specify an expression of x +
y.
Calculated
The transform will generate an output matrix named Calculated with the value at each
position being the point by point sum of the Raw (1) and Raw (2) matrices values. With the
example data provided above, the results are as follows (also displayed here to 3 decimal
places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.568 0.524 2.318 2.333 1.419 1.434 1.375 1.407 0.326 0.329 1.668 1.699
B 0.671 0.641 2.083 2.102 1.407 1.419 1.395 1.422 0.335 0.349 0.993 0.972
C 0.793 0.785 0.682 0.717 1.407 1.457 1.415 1.427 0.329 0.329 0.821 0.830
D 1.021 0.989 0.578 0.581 1.381 1.400 1.430 1.460 0.314 0.307 0.691 0.709
E 1.320 1.285 0.498 0.579 1.425 1.436 1.447 1.476 0.312 0.316 1.781 1.756
F 1.639 1.657 0.580 0.611 1.397 1.423 1.439 1.408 0.327 0.315 1.008 0.991
G 2.002 2.014 0.778 0.806 1.419 1.441 1.509 1.517 0.331 0.337 0.853 0.871
H 0.469 0.479 0.678 0.676 1.432 1.467 0.324 0.335 0.330 0.334 0.706 0.694
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Expression by Matrix
[Input: Endpoint
Output: Endpoint]
This transform evaluates an expression defined in terms of x for all data points (excluding
flagged samples).
Properties
Settings
Matrix
Expression
The expression to evaluate, where a single x variable is set to value of each
position of the input matrix
If the expression does not contain the x variable, the transform will output the same
evaluation result for all positions. For example, if you enter {2^3+4}, the resulting
matrix will contain 12 in each position.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to square each measurement.
Here is some example raw data displayed here to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
The Matrix Expression setting is specified with expression of x*x.
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Calculated
The transform will generate an output matrix named Calc with the value at each position
being the square of the input data. With the example data provided above, the results are as
follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.132 0.104 4.431 4.511 1.471 1.503 1.343 1.416 0.012 0.013 2.140 2.193
B 0.207 0.194 3.493 3.565 1.447 1.484 1.392 1.454 0.017 0.017 0.618 0.590
C 0.345 0.331 0.225 0.264 1.454 1.535 1.447 1.464 0.013 0.013 0.376 0.392
D 0.650 0.599 0.132 0.143 1.362 1.409 1.467 1.540 0.010 0.011 0.239 0.240
E 1.221 1.134 0.078 0.135 1.476 1.501 1.520 1.593 0.012 0.013 2.493 2.399
F 2.031 2.088 0.143 0.169 1.430 1.454 1.503 1.438 0.012 0.012 0.627 0.601
G 3.208 3.251 0.314 0.356 1.452 1.508 1.711 1.706 0.016 0.016 0.425 0.434
H 0.064 0.073 0.214 0.221 1.474 1.590 0.013 0.013 0.013 0.013 0.240 0.228
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Expression by Position
[Input: Endpoint
Output: Endpoint]
This transform enables an expression to be defined for each position in terms of x (where x
refers to the value of position on the input matrix). This is useful for applying various tests to
different samples simultaneously.
Properties
Settings
This allows different expressions to be configured and calculated on the positions of the same
index on different plates. Expressions are stored by container (1-based values are used for
each container separately).
Click on a drop-down to open a Position Expressions Editor with grid-based view of input
data. The configuration of the grid depends on the selected layout, thus each position’s
background is colored to match its sample type.
If the protocol contains >1 plate/analyte, select the one to specify expressions
for from a drop-down list in the upper right corner of the Editor. To maximize
its view, click on the arrow button in the lower left corner of the Editor.
Within each grid you can select and specify an expression in terms of x for:
l a single position.
l multiple positions (within a single plate) to set to a same expression.
Multiple positions can be selected by:
Position
Expressions
l dragging the mouse (for rectangular selection).
l using Ctrl to select random positions.
l using Shift to select the end position (rectangular selection).
Paste, Copy, and Clear commands are supported.
For positions with default (simply x) or empty expression, the output position
is equal to the input position.
Example
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Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to multiply positions from 1 to 5 by 10,
positions 10 and 96 by 100, and position 50 by 2.
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Provide expressions for positions in Position Expressions Editor:
l A1, A2, A3, A4, A5 with the expression x*10
l A10 and H12 with the expression x*100
l E2 with the expression x*2
Calculated
The transform will generate an output matrix named Calculated with the resulting values
being the product of input data and specified expressions. With the example data provided
above, the results are as follows (also displayed here to 3 decimal places):
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1
2
3
4
5
6
7
8
9
10
11
12
A 3.640 3.230 21.050 21.240 12.130 1.226 1.159 1.190 0.110 11.600 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 2.130 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 47.700
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Factor
[Input: Endpoint
Output: Endpoint]
This transform multiplies data points by a common factor. The factor can be specified as a
numeric value or as an expression to evaluate.
Properties
Settings
The value to multiply all data points by. This can be specified as a number or
an expression to evaluate.
Factor
Example:
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to multiply each measurement by a common
factor of 2.
Here is some example raw data displayed here to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
The Factor setting for this example is specified as 2.
Factored
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The transform will generate an output matrix named Factored with the value at each position
being the product of the input data and the factor. With the example data provided above, the
results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.728 0.646 4.210 4.248 2.426 2.452 2.318 2.380 0.220 0.232 2.926 2.962
B 0.910 0.882 3.738 3.776 2.406 2.436 2.360 2.412 0.264 0.260 1.572 1.536
C 1.174 1.150 0.948 1.028 2.412 2.478 2.406 2.420 0.226 0.232 1.226 1.252
D 1.612 1.548 0.728 0.756 2.334 2.374 2.422 2.482 0.202 0.212 0.978 0.980
E 2.210 2.130 0.558 0.734 2.430 2.450 2.466 2.524 0.222 0.228 3.158 3.098
F 2.850 2.890 0.756 0.822 2.392 2.412 2.452 2.398 0.218 0.216 1.584 1.550
G 3.582 3.606 1.120 1.194 2.410 2.456 2.616 2.612 0.252 0.254 1.304 1.318
H 0.506 0.540 0.926 0.940 2.428 2.522 0.230 0.232 0.230 0.230 0.980 0.954
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Matrix Difference
Input: Dual Endpoint
Output: Endpoint
This transform calculates the difference between two input matrices sample-by-sample.
If either of the matrix inputs are flagged, the result will be set to Flagged.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw (1) and Raw (2) matrices. In this example we would like to find the difference
between values in these matrices.
Here is some example raw data displayed here to 3 decimal places:
Raw (1)
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Raw (2)
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1
2
3
4
5
6
7
8
9
10
11
12
A 0.204 0.201 0.213 0.209 0.206 0.208 0.216 0.217 0.216 0.213 0.205 0.218
B 0.216 0.200 0.214 0.214 0.204 0.201 0.215 0.216 0.203 0.219 0.207 0.204
C 0.206 0.210 0.208 0.203 0.201 0.218 0.212 0.217 0.216 0.213 0.208 0.204
D 0.215 0.215 0.214 0.203 0.214 0.213 0.219 0.219 0.213 0.201 0.202 0.219
E 0.215 0.220 0.219 0.212 0.210 0.211 0.214 0.214 0.201 0.202 0.202 0.207
F 0.214 0.212 0.202 0.200 0.201 0.217 0.213 0.209 0.218 0.207 0.216 0.216
G 0.211 0.211 0.218 0.209 0.214 0.213 0.201 0.211 0.205 0.210 0.201 0.212
H 0.216 0.209 0.215 0.206 0.218 0.206 0.209 0.219 0.215 0.219 0.216 0.217
If the data point values of the Raw (2) are greater than the values of Raw (1), the
resulting matrix will contain negative values.
Difference
The transform will generate an output matrix named Difference with the value at each
position being the subtraction between values of the Raw (1) and Raw (2) matrices. With the
example data provided above, the results are as follows (also displayed here to 3 decimal
places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.160 0.122 1.892 1.915 1.007 1.018 0.944 0.973 -0.106 -0.097 1.258 1.263
B 0.239 0.241 1.656 1.674 0.999 1.017 0.965 0.990 -0.071 -0.089 0.579 0.564
C 0.381 0.365 0.266 0.311 1.005 1.021 0.991 0.993 -0.103 -0.097 0.405 0.422
D 0.591 0.559 0.150 0.175 0.953 0.974 0.992 1.023 -0.112 -0.095 0.287 0.271
E 0.890 0.845 0.060 0.155 1.005 1.014 1.019 1.048 -0.090 -0.088 1.377 1.342
F 1.211 1.234 0.176 0.211 0.995 0.989 1.013 0.990 -0.109 -0.099 0.576 0.559
G 1.580 1.592 0.342 0.389 0.991 1.016 1.107 1.095 -0.079 -0.083 0.451 0.447
H 0.037 0.061 0.248 0.264 0.996 1.055 -0.094 -0.103 -0.100 -0.104 0.274 0.260
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Median
[Input: Endpoint
Output: Endpoint]
This transform calculates the median of the replicates in each sample group (flagged
replicates are not included).
The median is the middle value of the values sorted into order. If there is an even
number of values, the result is the average between the two middle values.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find median value for each measurement
sample group.
Here is some example raw data displayed here to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
The example uses a layout with Unknowns in triplets (e.g., Unknown1 in positions from A1
to A3, Unknown2 in from A4 to A6, etc.). I.e.:
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Unknown
Median
The transform will generate an output matrix named Median with the value at each position
being the median of replicates of each sample group. With the example data provided above,
the results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.364 0.364 1.226 1.226 1.226 1.159 1.159 1.159 1.463 1.463 1.463
B 0.455 0.455 0.455 1.218 1.218 1.218 1.180 1.180 1.180 0.768 0.768 0.768
C 0.575 0.575 0.575 1.206 1.206 1.206 1.203 1.203 1.203 0.613 0.613 0.613
D 0.774 0.774 0.774 1.167 1.167 1.167 1.211 1.211 1.211 0.489 0.489 0.489
E 1.065 1.065 1.065 1.215 1.215 1.215 1.233 1.233 1.233 1.549 1.549 1.549
F 1.425 1.425 1.425 1.196 1.196 1.196 1.199 1.199 1.199 0.775 0.775 0.775
G 1.791 1.791 1.791 1.205 1.205 1.205 1.306 1.306 1.306 0.652 0.652 0.652
H 0.270 0.270 0.270 1.214 1.214 1.214 0.115 0.115 0.115 0.477 0.477 0.477
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Percentage Coefficient of Variation
[Input: Endpoint
Output: Endpoint]
This transform calculates the %CV of the replicates in each sample group (flagged replicates
are not included).
The %CV is the Standard Deviation divided by the sample group mean and
multiplied by 100. (If the mean or the Standard Deviation is 0, then the result is
forced to 0.)
For groups with less than 2 not-flagged replicates, the result for the group will be set
to Flagged.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find the percentage coefficient of variation
for each sample group of the input matrix.
Here is some example raw data displayed here to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
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The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
Standard
Blank
Control
Unknown
%CV
The transform will generate an output matrix named %CV with the value at each position
being the percentage coefficient of variation in each sample group. The %CV is useful for
measuring the deviation between the replicates. The %CV is calculated for each sample group
as the % of the standard deviation of the replicate measurements divided by their mean.
Typically, for replicates one might expect a %CV value to be < 15. A value greater than this
might indicate a preparation or layout error. Acceptable limits depend on the application.
With the example data provided above, the results are as follows (also displayed to 3 decimal
places):
l Sample Table
Here is a Sample Table snippet, which lists the details of the calculations. For each sample, the
following details are reported:
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l Matrix Table
Here all calculated %CV data is displayed in a Matrix Table.
1
2
3
4
5
6
7
8
9
10
11
12
A 8.440 8.440 0.635 0.635 0.754 0.754 1.866 1.866 3.755 3.755 0.865 0.865
B 2.210 2.210 0.715 0.715 0.876 0.876 1.541 1.541 1.080 1.080 1.638 1.638
C 1.460 1.460 5.726 5.726 1.909 1.909 0.410 0.410 1.853 1.853 1.484 1.484
D 2.864 2.864 2.668 2.668 1.202 1.202 1.730 1.730 3.416 3.416 0.144 0.144
E 2.607 2.607 19.265 19.265 0.580 0.580 1.644 1.644 1.886 1.886 1.356 1.356
F 0.986 0.986 5.915 5.915 0.589 0.589 1.575 1.575 0.652 0.652 1.534 1.534
G 0.472 0.472 4.523 4.523 1.337 1.337 0.108 0.108 0.559 0.559 0.755 0.755
H 4.597 4.597 1.061 1.061 2.686 2.686 0.612 0.612 0.000 0.000 1.901 1.901
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Percentage
[Input: Endpoint
Output: Endpoint]
This transform calculates a percentage value for each sample with respect to the specified 0%
and 100% values.
Properties
Settings
P0 and P100 can be defined as sample groups, evaluated in the context of the container.
For example, if a multiple container layout contains 2 containers of data, both container 1 and
container 2 include 2 Control groups (but in different places on each container). If P0 is
defined as Control1 and P100 is Control2, then when the analysis is performed, the %s for
container 1 will be calculated using Control1 and Control2 on container 1, whereas the %s
for container 2 will calculate %s using the Control1 and Control2 from container 2.
P0
The value treated as "0%." This can be defined as a number or as an
expression. All values less than P0 will have negative value in resulting matrix.
P100
The value treated as "100%." This can be defined as a number or as an
expression. All values greater than P100 will have values greater than 100 in
resulting matrix.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to calculate each measurement percentage
value with respect to 0-3 range.
Here is some example raw data displayed here to 3 decimal places:
Raw
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1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
To calculate all percentage values in this matrix, specify a P0 of 0 and P100 of 3.
%
The transform will generate an output matrix named %. The value at each position will equal
its percent rank with respect to the specified 0% and 100%. With the example data provided
above, the results are as follows (displayed here to 2 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 12.13 10.77 70.17 70.80 40.43 40.87 38.63 39.67 3.67 3.87 48.77 49.37
B 15.17 14.70 62.30 62.93 40.10 40.60 39.33 40.20 4.40 4.33 26.20 25.60
C 19.57 19.17 15.80 17.13 40.20 41.30 40.10 40.33 3.77 3.87 20.43 20.87
D 26.87 25.80 12.13 12.60 38.90 39.57 40.37 41.37 3.37 3.53 16.30 16.33
E 36.83 35.50 9.30 12.23 40.50 40.83 41.10 42.07 3.70 3.80 52.63 51.63
F 47.50 48.17 12.60 13.70 39.87 40.20 40.87 39.97 3.63 3.60 26.40 25.83
G 59.70 60.10 18.67 19.90 40.17 40.93 43.60 43.53 4.20 4.23 21.73 21.97
H 8.43 9.00 15.43 15.67 40.47 42.03 3.83 3.87 3.83 3.83 16.33 15.90
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Replicate Outlier Removal
[Input: Endpoint
Output: Endpoint]
This transform flags up to a specified number of outlier data points in each sample group. The
output matrix values match the input matrix values with any outlier(s) flagged. Outliers are
determined and removed as follows:
For each sample group, the transform:
1. Calculates the %CV of its replicates (including only the replicates not already flagged).
2. If the %CV is greater than the specified %CV Threshold, then the replicate furthest
from the mean is removed.
These steps are repeated until either the new %CV of the replicates that are not flagged is
below the threshold, or until the specified number of outliers to remove has been removed.
This transform can be used to perform automatic outlier rejection (useful when
samples are measured in replicate groups of 3 or more).
Properties
Settings
Outliers To
Remove
Specifies the maximum number of outliers to remove. The minimum and
default value for this setting is 1.
%CV
Threshold
Defines threshold percentage coefficient of variance. Data point with superior
or inferior values will be flagged. The default value is 15%.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to remove 1 outlier from each sample group.
Here is some example raw data displayed to 3 decimal places:
Raw
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1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
The example uses a layout with Unknowns in columns (e.g., Unknown1 vertically at
positions from A1to H1, Unknown2 from B2 to H2, etc.). I.e.:
Unknown
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To flag 1 outlier data point for each sample group, specify an Outliers To Remove as 1 and
%CV Threshold as 15%.
Calculated
The transform will generate an output matrix named Calculated with the value at each
position matching the input data and outlier data points flagged. With the example data
provided above, the results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
If there are only 2 remaining replicates (before the flag), both of them will be flagged
and a message will be added to the calculation log.
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Standard Curve Fit
[Input: Endpoint
Output: Endpoint]
This transform:
1. Plots the specified concentration values (X) against the measurement data (Y) for each
standard
2. Fits a curve to the standard data points (optionally using weight methods)
3. Calculates concentration values from the curve
4. Determines LLOQ and ULOQ using the specified quantification limits
5. Optionally computes any additional values from the curve (such as LLD)
To use Standard Curve Fit transform select or create a layout with a minimum of 2
Standard sample groups.
Properties
Axes
X Axis Title A name of the X axis on the chart
A type to plot an X axis in a chart:
X Axis Type l Logarithmic
l Linear
Y Axis Title A name of the Y axis on the chart
A type to plot an Y axis in a chart:
Y Axis Type l Logarithmic
l Linear
Calibrators
Concentrations Defines the X value for each point of the chart.
Standard Type Defines a sample type to treat as a standard.
Fit
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Defines how the output matrix results are calculated from the computed curve
for each sample (i.e., is a result calculated from the curve for each replicate or
for the average of the replicates?). The options are:
Calculate
From
l Replicates – Every position will contain the result of X, calculated from
value of every replicate (Y).
l Average – Replicates from the same sample contain the result of X,
calculated from averaged value of replicates (Y).
Any flagged positions on the input are also flagged in the output.
Fit Method
The curve-fitting method to construct a curve for your data based on the
specified Standard Type.
You can also use the Best Fit feature to plot the most appropriate fit for your
data automatically.
When the Best Fit feature is selected, to construct the curve, you can
additionally specify the scoring method to be used in evaluating the goodness
of applied fit methods. I.e.:
l R²
Fit Score
Method
l R² with Low-End Accuracy
l R² with High-End Accuracy
l R² with Low-End and High-End Accuracy
l 1/Standard-Error
l 1/P-Value
Defines which points are used as the calibrators to fit the curve to:
Fit To
Standard
l Replicates – All replicate points from each Standard sample group are
used as calibrators.
l Average – Average of the replicates from each Standard sample group
are used as the calibration points for the curve.
Weight
Method
Specifies the weight function for the transform.
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Determines how the X values are interpreted in the transform:
l None – The X values remain as they are.
X
Transform
l Log10 – Each X point is taken as a common logarithm of X.
l Log2 – Each X point is taken as a binary logarithm of X.
l LogN – Each X point is taken as a natural logarithm of X.
Determines how the Y values are interpreted in the transform:
l None – The Y values remain as they are.
Y
Transform
l Log10 – Each Y point is taken as a common logarithm of Y.
l Log2 – Each Y point is taken as a binary logarithm of Y.
l LogN – Each Y point is taken as a natural logarithm of Y.
Quantification Limits
Accuracy
(±%)
Defines a percentage limit of accuracy for Standard Type sample groups.
This is used to verify whether the accuracy of the calibrators' fits in required
limits. The default value is 20%.
Precision
(%CV)
Defines a %CV precision limit. This is used to verify whether %CV for each
Standard Type sample group is lower than required limit. The default value
is 20%.
The Quantification limits properties are used to determine the Lower Limit Of Quantification
(LLOQ) and Upper Limit Of Quantification (ULOQ) from the fit. The LLOQ and ULOQ are
computed and added to the report automatically.
The method used to calculate LLOQ and ULOQ can be overridden by setting named
evaluations with corresponding Name settings. In this case the custom calculated values will
replace the default values.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to construct a curve fit and calculate
concentrations for input matrix.
Here is some example raw data displayed to 3 decimal places:
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Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.192 0.199 0.347 0.346 0.325 0.321 0.803 0.797 1.589 1.593 0.241 0.246
B 0.293 0.300 0.586 0.584 0.800 0.793 0.402 0.401 0.839 0.837 0.562 0.564
C 0.537 0.534 0.549 0.547 0.878 0.875 0.471 0.471 0.759 0.756 1.039 1.043
D 0.858 0.855 0.320 0.325 0.531 0.530 1.552 1.551 0.429 0.427 0.424 0.424
E 1.523 1.527 0.486 0.490 1.289 1.283 1.430 1.438 1.494 1.501 1.427 1.429
F 1.622 1.621 0.621 0.626 0.667 0.663 1.350 1.347 0.631 0.634 1.695 1.694
G 0.001 0.001 0.356 0.352 0.418 0.412 1.172 1.174 1.572 1.570 0.595 0.590
H 0.297 0.297 0.796 0.801 1.517 1.515 0.960 0.965 0.534 0.527 0.585 0.584
Layout
The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
Standard
Blank
Control
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To configure a Standard Curve Fit for this matrix, provide following settings:
1. Set X Axis Title to Concentration.
2. Set X Axis Type to Logarithmic.
3. Set Y Axis Title to Raw.
4. Set X Axis Type to Linear.
5. Set Concentrations in Standard1 to 0.125 and a Series to multiply by 2.
6. Set Standard Type to Standard.
7. Set Calculate From to Average.
8. Select a Fit Method of 4PL.
9. Set Fit To Standard to Average.
10. Select a Weight Method of None.
11. Set a X Transform to None.
12. Set a X Transform to None.
13. Set Accuracy (±%) to 20%.
14. Set Precision (%CV) to 20%.
Concentration
The transform will generate an output matrix named Concentration with concentrations (X)
calculation results for all samples using the curve fit. With the example data provided above,
the results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A Flagged Flagged 0.352 0.352 0.318 0.318 0.844 0.844 2.890 2.890 0.153 0.153
B 0.274
0.274
0.623 0.623 0.841 0.841 0.423 0.423 0.886 0.886 0.600 0.600
C 0.572
0.572
0.585 0.585 0.928 0.928 0.503 0.503 0.799 0.799 1.131 1.131
D 0.906
0.906
0.317 0.317 0.566 0.566 2.561 2.561 0.454 0.454 0.450 0.450
E 2.390
2.390
0.521 0.521 1.550 1.550 1.969 1.969 2.240 2.240 1.947 1.947
F 3.243
3.243
0.662 0.662 0.704 0.704 1.702 1.702 0.671 0.671 5.174 5.174
G Flagged Flagged 0.362 0.362 0.439 0.439 1.332 1.332 2.710 2.710 0.630 0.630
H 0.275
0.275
0.843 0.843 2.338 2.338 1.030 1.030 0.566 0.566 0.622 0.622
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Click here to learn about Multiple Plate Standard Curve Fit Analysis.
Example Additional XML Evaluation and Flagging Configuration
In addition to plotting a curve fit and calculating concentrations, additional values can be
obtained with Standard Curve Evaluations. Moreover, with Standard Curve Custom Flag
Labels, you can exclude from analysis and mark with a custom label samples that are, for
example, outside the range of the standard calibrator.
In this example (using the data provided above):
l Lower Limit of Detection (LLD) is computed from the curve by calculating the
concentration at the average of the blank measurements + 3 * standard deviation of the
blank positions.
l The Name attribute for LLD is specified and is used as a variable to flag condition x<LLD.
XML Configuration
The set attributes are highlighted in yellow.
<Settings>
<StandardCurveFitStandardType="2" FitMethod="4PL" Concentrations="0.25,0.5,1,2,4,8"
XAxisTitle="Concentration" YAxisTitle="OD" FitToStandard="Average"
CalculateFrom="Average">
<Evaluations>
<EvaluationType="CalcY" Expression="Blank + (3 * sd(Blank))"
Description="My LLD"Name="LLD" />
</Evaluations>
<Flags>
<FlagType="Input"Condition="x &lt; LLD"Label="Below LLD" />
</Flags>
</StandardCurveFit>
</Settings>
Upon calculating data with the aforementioned XML configuration defined, the LLD evaluation
is computed and added to the report below an interactive chart. It is displayed in the table
downward to LLOQ and ULOQ, which are calculated from the curve automatically.
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LLOQ
0.25
ULOQ
2
My LLD
0.222063
With the example data provided above, the results are computed as follows:
A
1
2
3
4
5
6
7
8
9
10
11
12
Below Below
0.352 0.352 0.318 0.318 0.844 0.844 2.890 2.890 0.153 0.153
LLD
LLD
B 0.274 0.274 0.623 0.623 0.841 0.841 0.423 0.423 0.886 0.886 0.600 0.600
C 0.572 0.572 0.585 0.585 0.928 0.928 0.503 0.503 0.799 0.799 1.131 1.131
D 0.906 0.906 0.317 0.317 0.566 0.566 2.561 2.561 0.454 0.454 0.450 0.450
E 2.390 2.390 0.521 0.521 1.550 1.550 1.969 1.969 2.240 2.240 1.947 1.947
F 3.243 3.243 0.662 0.662 0.704 0.704 1.702 1.702 0.671 0.671 5.174 5.174
G
Below Below
0.362 0.362 0.439 0.439 1.332 1.332 2.710 2.710 0.630 0.630
LLD
LLD
H 0.275 0.275 0.843 0.843 2.338 2.338 1.030 1.030 0.566 0.566 0.622 0.622
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Standard Deviation
[Input: Endpoint
Output: Endpoint]
This transform calculates the standard deviation of the replicates in each sample group
(flagged replicates are not included). The sample of population method is used.
The standard deviation is a measure of how widely the replicate values are dispersed
from the average of the group.
For groups with less than 2 not-flagged replicates, the result for the group will be set
to Flagged.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find the deviation between replicates in each
sample group.
Here is some example raw data displayed here to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
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Standard
Blank
Control
Unknown
SD
The transform will generate an output matrix named SD with the value at each position being
the product of the input data and deviation between positions of each sample group. With the
example data provided above, the results are as follows (also displayed here to 3 decimal
places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.029 0.029 0.013 0.013 0.009 0.009 0.022 0.022 0.004 0.004 0.013 0.013
B 0.010 0.010 0.013 0.013 0.011 0.011 0.018 0.018 0.001 0.001 0.013 0.013
C 0.008 0.008 0.028 0.028 0.023 0.023 0.005 0.005 0.002 0.002 0.009 0.009
D 0.023 0.023 0.010 0.010 0.014 0.014 0.021 0.021 0.004 0.004 0.001 0.001
E 0.028 0.028 0.062 0.062 0.007 0.007 0.021 0.021 0.002 0.002 0.021 0.021
F 0.014 0.014 0.023 0.023 0.007 0.007 0.019 0.019 0.001 0.001 0.012 0.012
G 0.008 0.008 0.026 0.026 0.016 0.016 0.001 0.001 0.001 0.001 0.005 0.005
H 0.012 0.012 0.005 0.005 0.033 0.033 0.001 0.001 0.000 0.000 0.009 0.009
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Standard Error of Mean
[Input: Endpoint
Output: Endpoint]
This transform calculates the standard error of the mean (SEM) of the replicates in each
sample group (flagged replicates are not included). The sample of population method is used.
The standard error of the mean quantifies how precisely the average of the replicates
estimates the true mean of the population. Lower values of SEM indicate more
precise estimates of the population mean. Typically, increasing the number of
replicates will result in a smaller SEM value and a more precise estimate.
For groups with less than 2 not-flagged replicates, the result for the group will be set
to Flagged.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find the SEM of each sample group.
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
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The example uses a layout with Unknowns in triplets (e.g., Unknown1 in positions from A1
to A3, Unknown2 in from A4 to A6, etc.). I.e.:
Unknown
SEM
The transform will generate an output matrix named SEM with the value at each position
being the product of the input data and the standard deviation divided by the square root of
the number of non-flagged replicates in each group. With the example data provided above,
the results are as follows (also displayed here to 3 decimal places):
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1
2
3
4
5
6
7
8
9
10
11
12
A 0.587 0.587 0.587 0.302 0.302 0.302 0.355 0.355 0.355 0.452 0.452 0.452
B 0.474 0.474 0.474 0.226 0.226 0.226 0.354 0.354 0.354 0.216 0.216 0.216
C 0.036 0.036 0.036 0.236 0.236 0.236 0.365 0.365 0.365 0.168 0.168 0.168
D 0.142 0.142 0.142 0.266 0.266 0.266 0.375 0.375 0.375 0.128 0.128 0.128
E 0.269 0.269 0.269 0.284 0.284 0.284 0.379 0.379 0.379 0.483 0.483 0.483
F 0.352 0.352 0.352 0.263 0.263 0.263 0.368 0.368 0.368 0.225 0.225 0.225
G 0.412 0.412 0.412 0.207 0.207 0.207 0.394 0.394 0.394 0.176 0.176 0.176
H 0.067 0.067 0.067 0.256 0.256 0.256 0.000 0.000 0.000 0.123 0.123 0.123
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Variance
[Input: Endpoint
Output: Endpoint]
This transform calculates the variance of the replicates in each sample group (flagged
replicates are not included). The sample of population method is used.
The variance is the sum of the squares of the difference of each replicate and the
mean divided by n-1 (where n is the number of the replicates).
For groups with less than 2 not-flagged replicates, the result for the group will be set
to Flagged.
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find the variance between measurements in
each sample group.
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
Layout
The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
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Standard
Blank
Control
Unknown
Variance
The transform will generate an output matrix named Variance with the values at each sample
group being the product of the input data variance. With the example data provided above,
the results are as follows (also displayed here to 3 decimal places):
1
2
3
4
5
6
7
8
9
10
11
12
A 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
C 0.000 0.000 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000
D 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
E 0.001 0.001 0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
F 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
G 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
H 0.000 0.000 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000
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General Transform Properties for XY Data
The transforms that operate on the XY Data are configured with:
l general settings for all transforms
l general settings for XY and XY Reduction transforms (described below)
l specific settings (if available)
General Settings for XY and XY Reduction Transforms
Data
This setting determines which values to use in the X chart axis and for
evaluations:
l Index – The 1-based position number of the data point in the collection.
l X – Depending on the measurements data type, this option is displayed as:
l X (Cycle) – For kinetic data with no time values, X equals the Index.
X Axis
l X (Time) – For kinetic data, if time values are stored, then this is the
time stamp associated with the data point.
l X (Wavelength) – For spectral data, if wavelength values are stored,
then this is the wavelength of the data point.
l Y – Each value is set to a Y value of the corresponding input matrix's data
point.
This setting determines which values to consider in the Y chart axis and for
evaluations:
l Index – The 1-based position number of the data point in the collection.
l X– Depending on the measurements data type, this option is displayed as:
Y Axis
l X (Cycle) – For kinetic data with no time values, X equals the Index.
l X (Time) – For kinetic data, if time values are stored, then this is the
time stamp associated with the data point.
l X (Wavelength) – For spectral data, if wavelength values are stored,
then this is the wavelength of the data point.
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l Y – Eeach value is set to a Y value of the corresponding input matrix's data
point.
Range
The range settings enable the specification of a subset of the data points to consider; only data
points within the specified range will be processed. When a range is set, data points outside
the specified range are excluded from the calculation. Thus, only the data points inside the
specified minimum and maximum range (that are also not flagged) are processed by the
transform.
The range can be defined by setting the X Minimum and X Maximum properties to a value
or expression:
A numerical value or expression that defines the minimum X data point to
X Minimum include within the subset. If this is not specified, then all data points less than
X Maximum are included.
X
Maximum
A numerical value or expression that defines the maximum X data point to
include within the subset. If this is not specified, then all data points greater
than X Minimum are included.
To refer to the position's corresponding data on an endpoint matrix, use a Matrix
Position Reference (e.g., #2) to refer to the data on the second matrix. This is useful to
set the range of calculations for each sample to be based on the results of previous
calculation steps.
In addition, the range can be adjusted interactively by positioning range bars. The range bars
are displayed as vertical dashed lines on the chart that you can either:
l Drag with mouse to correct range values
l Double-click on to reset minimum or maximum value
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XY Transforms
XY transforms operate on matrices of data that consist of XY data and result in a new XY data
matrix.
For example, the XY Baseline Correction transform can be used to remove the inherent
baseline from each sample to result in a new XY plot with the measurements shifted down the
Y axis.
XY Data
Throughout MyAssays Desktop, we refer to measurements that yield a 2-dimensional data
sets for each sample in general terms as "XY Data". This encompasses:
l Kinetic data with measurements made over time (with or without time values).
l Spectral data measured at different wavelengths.
l Scan data measured as a single line (with a distance from an origin).
XY Axes
With certain measurement types, there might be different data available to plot on the X and Y
axes. For example, with kinetic data measured over time you might want to use the time
values as the X axis or the cycle number instead. The axis settings specify what data should be
processed by the transform. For further details you can refer to the General Transform
Properties for XY Data subsection.
Flagged XY Data Points
Any data point that is part of an XY data set can be flagged. Flagged data points are excluded
from the calculation.
For example, with the XY Baseline Correction transform, if the baseline is computed using the
average of the 4 lowest measurements but the lowest measurement is flagged, then the
baseline will be computed using the next 4 lowest data points.
In addition to each data point, each position can also be flagged to completely exclude the
sample from any further analysis. This follows the same convention as for Flagged Endpoint.
XY Range
By default, XY data transform calculations are performed on all XY data points that are not
flagged. However, it is also possible to define a range of points to process. This is useful if the
area of interest is only a subset of the measurements. The range minimum and maximum
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values can be set as an absolute value or as an expression (that can in turn refer to other
computed results).
For example, the XY Expression transform could be configured to work with input XY data that
has 100 data points and to use only points >=20 and <= 80. In this case, the resulting output
XY matrix would contain only this subset.
Refer to the General Transform Properties for XY Data subsection for more details.
XY Transforms
Transform
Operation
Baseline
Correction
This transform removes the inherent variable background, or baseline, from
each input position.
Blank
Correction
Subtracts the average of specified Blank group(s) from each input position.
Expression
(Dual)
Evaluates an expression in terms of x and y, where variables are set to the
value of each position of the first (x) and second (y) input matrices.
Expression
Evaluates the specified expression for each input position.
Fit
Fits a curve to each used XY plot.
Join
Creates new combined XY plot from two input XY matrices for each position
by appending data points from the second matrix to those of the first.
Outlier
Removal
Flags data points that are considered to be outliers. The values of the output
matrix match the input matrix (the only difference being that outlier(s) might
be flagged).
Replicates
Average
Calculates the XY average plot for each sample group.
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XY Baseline Correction
[Input: XY
Output: XY]
This transform removes the inherent variable background (or baseline) from each XY position.
Flagged positions or points are not included in the calculations. The operation is only
performed on data within specified range.
Properties
Settings
The method for a baseline correction:
Method
l Average Between X1 And X2 – Correct points by the average Y of the
not-flagged points between and including points X Index 1 and X Index
2.
l Average Of N Lowest – Correct points by the average Y of the lowest
not-flagged N.
l Proportional N1 Lowest and N2 Highest – Proportional percentage
between average of the N1 lowest and N2 highest not-flagged points.
The visibility of the following settings depend on the selected Method. For
example, if you choose the Average Of N Lowest, then only the N setting will be
displayed.
These settings are used in the Average Between X1 And X2 method.
X Index 1
X Index 2
These are 1-based index numbers and apply to the data sets with or without
time values. Only not-flagged points that are <= X Index 1 and >= X Index
2 are used. X Index 1 and X Index 2 must be >=1 and X Index 2 must be
>= X Index 1.
If there are no included points between X Index 1 and X Index 2, then no
correction will be made and the output position will be flagged.
This setting is used in the Average Of N Lowest method.
N
It is the number of lowest not-flagged data points within a subset. If the
number of included points < N, then the output position will be flagged. If the
number of included points = N, then the result is that all points are corrected
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by the average of all points.
These settings are used in the Proportional N1 Lowest and N2 Highest
method.
N1
N2
N1 is the number of the lowest points, N2 is the number of the highest points
to used for correction. N1 and N2 must be >= 1. The average of each of these
is computed. Then each Y value is scaled between these points (e.g., if a point
equals the average of the lowest, then it is set to 0; or if a point equals the
average of the highest, it is set to 100).
Also, note results here are clipped, this means that all results will fall within
the range 0 and 100, so values which are calculated to < 0 or > 100 will be set
to 0 and 100, respectively.
If the average of the lowest values is equal to the average of the highest
values, then this cannot be calculated and the output position will be flagged.
If there are less than N1 or N2 valid readings, then the output position will be
flagged.
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XY Blank Correction
[Input: XY
Output: XY]
This transform subtracts the average of a specified blank group (or groups) from each sample
on a point-by-point basis. This is useful for subtracting background noise from
measurements.
The background plot is first determined by creating an average XY plot of all replicates of the
blank group. This background plot is then subtracted from each associated sample on a pointby-point basis.
Flagged positions or points are not included in the calculations. The operation is only
performed on data within specified range.
Properties
Settings
This transform requires a layout with at least 1 Blank group.
Blank
Group
The group number of the sample to use for correction. This can be defined as
a number or as an expression. The expression can refer to x which is the
group number of the sample to be corrected.
For example, set this to x to perform a 1-to-1 correction where each group is
corrected by the sample of the specified blank type with the same group
number.
Blank Type The sample type used as the blank.
Example: Single Blank
Let's assume that we have some XY measurement data for a 12x8 microplate stored in the
Raw matrix. In this example we would like to perform a blank correction using the average of
one blank group. This blank group itself is measured in duplicate. The mean of these replicates
will be subtracted from all other samples.
Layout
The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
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Standard
Blank
Control
Unknown
To subtract the mean of the blank positions from the measurements, specify Blank Group to
1 and Blank Type to Blank.
Blank Corrected
The transform will generate an output matrix named Blank Corrected with the value at each
position being the subtraction of the average of Blank1 sample group from the input data.
Example: 1-1 Blank Correction
Let's assume that we have some XY measurement data for a 12x8 microplate stored in the
Raw matrix. In this example we would like to perform a 1-to-1 correction, where each sample
group is corrected by the blank that it is associated with. The blank numbering used in the
layout defines the associations.
Layout
The example uses a layout with 16 Unknowns groups (with Unknown1 in positions from A1
to A5, Unknown2 in positions from B1 to B5, etc.) and 16 Blank singlets. I.e.:
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Specify Blank Group to x and Blank Type to Blank to perform 1-1 correction.
With this specified, the value of each blank will be subtracted from every replicate of
associated sample group. In other words, Blank1 (stored in A6) value will be subtracted from
A1, A2, A3, A4, A5 (the members of Unknown1), and from A6 itself.
Blank Corrected
The transform will generate an output matrix named Blank Corrected with the value at each
position being the subtraction of the associated blank value from the input data. Therefore,
calculated matrix will contain blank corrected Unknown values and all Blanks will be 0.
Advanced Example: By-Row Blank Correction
Let's assume that we have some XY measurement data for a 12x8 microplate stored in the
Raw matrix. In this example we would like to configure blank correction on a row-by-row
basis to blank correct each sample by the average of the two blank samples of the same row.
Layout
The example uses a layout with 80 singlet Unknowns and 8 Blank duplicates for each row
(with Blank1 in positions A1 and A12, Blank2 in positions B1 and B12, etc.) I.e.:
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Blank
Unknown
The Blank Group setting uses the expression:
1 + floor ((x -1) / (LayoutWidth()-2),1)
This converts the group number of the sample to blank correct (x) to the blank number on its
row. In other words, for x=1 this means 1+floor((1-1)/10),1), floor=0 and the Blank Group
is taken as 1. For the first row, x is in range from 1 to 10 and the expression outputs 1, thus
for Unknowns from 1 to 10 the average of Blank1 is subtracted.
For the second, x is from 11 to 20, floor=1, so the Blank2 group is subtracted from
Unknowns11-20, etc.
Blank Corrected
The transform will generate an output matrix named Blank Corrected with the value at each
position being the subtraction of the average of the associated Blank group from the input
data.
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XY Expression (Dual)
[Input: Dual XY
Output: XY]
This transform evaluates an expression defined in terms of x and y for each not-flagged input
XY data point within the specified range. In this case x refers to the data for the sample on the
first input matrix and y refers to the data for the same sample on the second input matrix.
This transform is useful when it is necessary to perform a common operation on two
sets of XY data. For example, this transform could be used to create a plot for each
sample, that is the difference or ratio of two sets of XY measurements.
Properties
Settings
The expression to evaluate, where variables are set to the value of each
position of the first (x) and second (y) input matrices. Default expression value
is x - y.
Expression
To refer to the position's corresponding data on an endpoint matrix,
use a Matrix Position Reference (e.g., #2) to refer to the data on the
second matrix.
Plot Input
on Chart
When the checkbox is checked, the chart includes both the input and the
calculated plot. If it is unchecked, the chart shows the calculated plot only.
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XY Expression
[Input: XY
Output: XY]
This transform evaluates a specified expression for each input XY data point (only points
within the specified range that are not flagged are processed). The resulting XY plot is
generated from this expression.
This transform is useful when it is necessary to perform an operation on all input
points to calculate a new plot for each sample. For example, this transform could be
used to subtract, correct, or multiply all plots by a specified value.
Properties
Settings
The expression to evaluate, where the y variable is set to the value of each
position of the input matrix.
Expression
Plot Input
on Chart
To refer to the position's corresponding data on an endpoint matrix,
use a Matrix Position Reference (e.g., #2) to refer to the data on the
second matrix.
When the checkbox is ticked, the chart includes both the input and the
calculated plot. If it is unchecked, the chart shows the calculated plot only.
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XY Fit
[Input: XY
Output: XY]
This transform fits a curve to each XY plot and generates a new XY plot from the calculated
curve.
The resulting plot is computed by calculating the Y value of each X point using the fitted curve.
(Only points within the specified range that are not flagged are processed.)
This transform can be used to smooth each XY plot using a particular curve-fitting
model. If you are interested in a fitting parameter of the curve fit (such as the slope of
linear regression or midpoint of 4PL), then use the XY Reduction Fit Parameter
transform.
Properties
Settings
Fit Method The curve fitting method to construct the curve (from each XY plot)
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XY Join
[Input: Dual XY
Output: XY]
This transform accepts two XY input matrices and creates new combined XY plot for each
position by appending data points from the second matrix to those of the first. (Any flagged
data points are not included in the output matrix.)
This transform can be used to combine two separate sets of XY measurements to a
single plot (for each sample) for further analysis or reporting.
Properties
Advanced, Units and Range settings do not apply to this transform.
If X is set as Index, then the first index of the joined matrix is 1 plus the last used
index.
If X is set as X, then the X values specified in each input matrix are used (unchanged)
in the output matrix.
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XY Outlier Removal
[Input: XY
Output: XY]
This transform flags data points that are considered to be outliers. The values of the output
matrix match those of the input matrix (the only difference being that any specified outlier(s)
are flagged).
For each XY plot, the transform:
1. Calculates the linear regression of the specified number of adjacent points (segment)
2. Uses the calculated line to calculate Y from each X in the segment
3. Compares each calculated Y with the original Y and flags the points where the %
difference is > than the specified threshold
This process is repeated across each sequential segment in the plot for adjacent point sets.
This transform can be used to automatically remove outlier points in XY data.
Properties
Settings
Num
The number of regression points used for each linear regression segment
Regression
when determining the maximum slope. The default and minimum value is 2.
Points
Defines the threshold % accuracy to flag outliers.
Threshold
It calculates absolute % difference between back fit value and the original y
value. The back fit is computed from slope and intercept for each x value in
the regression set. If this % difference is > the specified Threshold, then the
data point is flagged on the output matrix.
Points that are flagged in one regression set are not included in
calculations of following regression sets.
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XY Replicates Average
[Input: XY
Output: XY]
This transform calculates the average XY (kinetic/spectral) plot for each sample group. Any
flagged data points are not included in the analysis.
As with other XY transforms, the calculations are performed on data within the specified
ranges only.
To use this transform, select a layout with at least one sample group that contains
more than one replicate.
Example
Let's assume that we have some XY measurement data for a 12x8 microplate stored in the
Raw matrix. In this example we would like to perform a blank correction using the average of
1 blank group. This blank group itself is measured in duplicate. The mean of these replicates
will be subtracted from all other samples.
Layout
The example uses a layout with Standards, Control, Blank, and Unknowns in duplicate (e.g.,
Standard1 in A1 and A2, Unknown1 at A3 and A4, Unknown2 at B3 and B4, etc.). I.e.:
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Standard
Blank
Control
Unknown
Average
The transform generates an XY output matrix named Average, where positions contain the
combined XY plot. This plot is calculated as the average of the XY plots for replicates in each
sample group.
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XY Reduction Transforms
XY transforms operate on matrices of data that consist of XY data and result in a new endpoint
data matrix.
For example, the XY Reduction Peak transform will compute the peak value of each XY plot.
XY Data
Throughout MyAssays Desktop, we refer to measurements that yield a 2-dimensional data
sets for each sample in general terms as "XY Data". This encompasses:
l Kinetic data with measurements made over time (with or without time values).
l Spectral data measured at different wavelengths.
l Scan data measured as a single line (with a distance from an origin).
XY Axes
With certain measurement types, there might be different data available to plot on the X and Y
axes. For example, with kinetic data measured over time you might want to use the time
values as the X axis or the cycle number instead. The axis settings specify what data should be
processed by the transform. For further details you can refer to the General Transform
Properties for XY Data subsection.
Flagged XY Data Points
Any data point that is part of an XY data set can be flagged. Flagged data points are excluded
from the calculation.
For example, with the XY Reduction Peak transform, if the peak is set to minimum and is
computed using the lowest measurement but the lowest measurement is flagged, then the
peak will be computed using the next lowest data point.
In addition to each data point, each position can also be flagged to completely exclude the
sample from any further analysis. This follows the same convention as for Flagged Endpoint.
XY Range
By default, XY data transform calculations are performed on all XY data points that are not
flagged. However, it is also possible to define a range of points to process. This is useful if the
area of interest is only a subset of the measurements. The range minimum and maximum
values can be set as an absolute value or as an expression (that can in turn refer to other
computed results).
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For example, the XY Reduction Average transform could be configured to work with input XY
data that has 100 data points and to use only points >=20 and <= 80. In this case, the
resulting output endpoint matrix would contain only mean value from this subset at each
position.
Refer to the General Transform Properties for XY Data subsection for more details.
XY Reduction
Transform
Reduces Data To
Average
The average of non-flagged data points within a subset at each position.
Fit Parameter
The specified parameter for non-flagged data points within specified range at
each position.
Maximum
Slope
Determined maximum slope between two adjacent points within specified
range at each position.
Peak
Determined peak (minimum or maximum) data point within the specified
range of the non-flagged points at each position.
Total
The sum of non-flagged data points within specified range at each position.
X At Y
The X value at which the specified Y value crosses the evaluated plot of nonflagged points within the specified range at each position.
X Between Y
The difference between the first two X points that equal or cross specified Y
value at each position.
Y At X
Determined Y value for a specified X value within the specified range of the
not-flagged points at each position.
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XY Reduction Average
[Input: XY data
Output: Endpoint]
This reduction transform computes the average Y value of each XY plot (within the specified
range of points to consider). Flagged positions or points are not included in the calculations.
Example
Let's assume that we have some XY measurement data for a 12x8 microplate stored in the
Raw matrix. In this example we would like to reduce data in each position to average of its
data points and store resulting value to corresponding position of an endpoint matrix.
Raw
Here is an XY matrix with each position containing 10 XY data points. Position data plots of
the matrix are displayed in Plate Scale.
Here the reduction of data in the A1 position is shown for illustration of the transform. The
raw data is displayed here to 3 decimal places:
A1 0.000;0.555 0.100;0.988 0.200;1.613 0.300;1.938 0.400;2.608 0.500;2.889 0.600;3.773
0.700;3.816 0.800;4.898 0.900;5.491
To reduce XY data to an average endpoint value:
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1. Specify Data: X Axis as X (Time), Y Axis as Y.
2. Leave X Minimum and X Maximum blank.
3. Press Calculate.
Average
The transform will generate an endpoint matrix named Average with the value at each
position being the averaged value of the Y input data points. With the example data provided
above, the resulting averaged value for the A1 plot is as follows (also displayed here to 3
decimal places):
2.857
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XY Reduction Fit Parameter
[Input: XY data
Output: Endpoint]
This reduction transform performs the selected curve fit on each XY plot and sets the result to
the value of a specified fit parameter. Points outside the specified range and flagged
positions/points are excluded from calculations.
This transform can be used to obtain the specific fitting parameter of a curve fit to
each plot (such as the slope of linear regression or midpoint of 4PL).
Properties
Settings
Fit Method The curve fitting method to construct a curve for your data
Parameter
Represents the Fit Transform Context Parameter to fit data points by
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XY Reduction Maximum Slope
[Input: XY data
Output: Endpoint]
This reduction transform finds the maximum slope of each XY plot. The result can be set to the
slope, or to the X or Y value at the maximum slope (absolute or relative to the start X).
This transform can be used in kinetic applications to calculate the time to reach a
maximum rate or to find the maximum rate in a reaction.
The maximum slope is determined by computing the linear regression of adjacent points
(using the specified number of linear regressions points) and determining the slope with the
maximum (upward or downward) value.
Flagged positions or points or those outside specified ranges are not included in the
calculations.
Properties
Settings
Determines the type of slope to be found by this transform. The settings
are:
Max is
l Auto – The maximum slope is determined from the general direction of
each plot by using the linear regression. If the slope of the plot is
positive, the Fastest Increase is used; if negative, the Fastest Decrease
is used.
l Fastest Increase – The maximum slope is the slope with the highest
positive value.
l Fastest decrease – The maximum slope is the slope with the greatest
negative value.
Num
Regression
Points
The number of regression points used for each linear regression segment
when determining the maximum slope. The default and minimum value is
2.
Specifies the result of the analysis. The settings are:
Result is
l Slope – The result is the slope of the determined maximum slope.
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l X At Centre Absolute – The result is the X at the center of the
determined maximum slope.
l Y At Centre Absolute – The result is the Y at the center of the
determined maximum slope.
l X At Centre Relative – The result is the X at the center of the
determined maximum slope relative to the first not-flagged data point
that is greater than X Minimum.
l Y At Centre Relative – The result is the Y at the center of the
determined maximum slope relative to the first not-flagged data point
that is greater than X Minimum.
l Extrapolated Y – A value of Y is calculated from the determined
maximum slope using the X value specified with X For Extrapolation.
When Extrapolated Y option in the Result Is setting is chosen, then this
X for
can be used to specify an expression used to determine the X value to
Extrapolation
calculate Y for.
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XY Reduction Peak
[Input: XY data
Output: Endpoint]
This reduction transform computes the peak of all values for each XY plot (within the specified
range of points to consider). Flagged positions or points are not included in the calculations.
The result can be set to the X or Y value at the peak, with the peak being defined as either the
minimum or maximum value.
Properties
Settings
This setting determines which data point is considered as the peak:
Peak
l Max – The point with the maximum Y value
l Min – The point with the minimum Y value
This setting determines which axis's coordinate is stored in resulting matrix:
Result
l X – Projection of Peak value on X axis
l Y – Projection of Peak value on Y axis
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XY Reduction Total
[Input: XY data
Output: Endpoint]
This reduction transform computes the sum of all Y values for each XY plot (within the
specified range of points to consider). Flagged positions or points are not included in the
calculations.
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XY Reduction X At Y
[Input: XY data
Output: Endpoint]
This reduction transform finds the X value at which the specified Y value (Cursor) crosses the
plot (within the specified range). If the Y position is in between two adjacent data points, then
an interpolated value is calculated from the bounding points.
X and Y values can be specified as a relative to the first point or as an absolute value. Flagged
positions and points are not included in calculations.
This transform can be used to determine the X value at the point where the
horizontal (defined by Y) value crosses each plot.
Properties
Settings
The expression evaluated for each position that represents the target in Y
value. The default value is 1.
Cursor
To refer to the position's corresponding data on an endpoint matrix,
use a Matrix Position Reference (e.g., #2) to refer to the data on the
second matrix.
This defines how the Cursor is treated for each position. The settings are:
l Absolute – The value specified by Cursor is the value to use in the
calculation of X.
Cursor
Type
l Relative From First Measured – The value of Cursor is added to the Y
value of the first point for the position (even if the first point is Flagged or
outside the range).
l Relative From First In Range – The value of Cursor is added to the Y
value of the first non-flagged data point within the range.
Specifies the result of the analysis. The settings are:
Result is
l Absolute – The computed X value.
l Relative From First Measured – The computed X value minus the X
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value of the first point for the position (even if the first point is Flagged or
out of range).
l Relative From First In Range – The computed X value minus the X value
of the first non-flagged data point within the range.
XY Reduction X Between Y
[Input: XY data
Output: Endpoint]
This reduction transform computes the difference between the first two X points at which the
specified Y value (Cursor) crosses or equals the plot.
Y value can be specified as a relative to the first point or as an absolute value. Flagged
positions and points and points outside the specified range are not included in calculations.
Properties
Settings
The expression evaluated for each position that represents the target in Y
value. The default value is 1.
Cursor
To refer to the position's corresponding data on an endpoint matrix,
use a Matrix Position Reference (e.g., #2) to refer to the data on the
second matrix.
This defines how the Cursor is treated for each position. The settings are:
l Absolute – The value specified by Cursor is the value to use in the
calculation of X.
Cursor
Type
l Relative From First Measured – The value of Cursor is added to the Y
value of the first point for the position (even if the first point is Flagged or
outside the range).
l Relative From First In Range – The value of Cursor is added to the Y
value of the first non-flagged data point within the range.
If the plot is not crossed or only crossed once, the result will be flagged.
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XY Reduction Y At X
[Input: XY data
Output: Endpoint]
This reduction transform finds the Y value at which the specified X value (Cursor) crosses the
plot (within the specified range). If the X position is in between two adjacent data points, then
an interpolated value is calculated from the bounding points.
The X value can be specified as a relative to the first point or as an absolute value. Flagged
positions and points are not included in calculations.
This transform can be used to determine the Y value at the point where the vertical
(defined by X) value crosses each plot (or more simply the Y at a given X).
Properties
Settings
The expression evaluated for X value of each position.
Cursor
To refer to the position's corresponding data on an endpoint matrix,
use a Matrix Position Reference (e.g., #2) to refer to the data on the
second matrix.
This defines how the Cursor is treated for each position. These settings are
used to handle scenarios where X values for each position may be out of sync
by adding an offset to the cursor. The settings are:
Cursor
Type
l Absolute – The value specified by the Cursor is the value to use in the
calculation of Y.
l Relative From First Measured – The Cursor value is added to the X
value of the first point for the position (even if the first point is Flagged or
out of range).
l Relative From First In Range – The Cursor value is added to the X value
of the first non-flagged data point within the range
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Curve Fitting
Methods
Four Parameter Logistic Regression (4PL)
This nonlinear symmetrical sigmoidal model is known as 4-parameter logistic regression
(4PL).
It is quite useful for dose response and/or receptor-ligand binding assays, or other similar
types of assays. As the name implies, it has 4 parameters that need to be determined in order
to “fit the curve.” The model fits data that makes a sort of S-shaped curve that is symmetrical
around its inflection point.
The equation for the model is:
Variables
In this model we have the following:
y = the dependent variable (i.e., what you measure as the signal)
x = the independent variable (i.e., what you control, such as dose, concentration, etc.)
Coefficients of the Fit
The 4 parameters consist of the following:
l a = the minimum value that can be obtained (i.e., what happens at 0 dose).
l d = the maximum value that can be obtained (i.e., what happens at infinite dose).
l c = the point of inflection (i.e., the value of x at the point on the S-shaped curve halfway
between a and d).
l b = Hill’s slope of the curve (i.e., this is related to the steepness of the curve at point c). It
could either be positive or negative.
The rearranged equation to solve x is:
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The a and d values might be flipped; however, a and d will always define the upper
and lower asymptotes (horizontals) of the curve. a and d are the same units at y. The
curve can only be used to calculate concentrations for signals within a and d.
Samples outside the range of the determined a and d cannot be calculated.
4PL and IC50
A common requirement is to calculate IC50 from the fit. The IC50 is the
concentration of an inhibitor where the response (or binding) is reduced by half.
There are actually two ways to do this depending on what you consider IC50 to be:
l Firstly, the midpoint of the sigmoid of the 4PL is equal to the c coefficient of
the 4PL. In this case you can simply look at the calculated c coefficient. Use this
method if you consider the midpoint of the sigmoid to be equal to IC50.
Mathematically, this is the case as it is the x point at exactly half way between
the two horizontal asymptotes.
l Alternatively, if the response is measured between 0 and 100% and you
consider IC50 to be where y = 50, then you can calculate where y = 50 using
the equation to solve x (above), substituting in the calculated coefficients.
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Linear Regression
It is a basic and easy regression model that fits data in a linear way.
Linear regression sets the linear relationship between the dependent variable (y) and a single
independent variable (x). This model provides starting approximation and, although is seldom
used for biological systems, is often used to visualize data relationship at a glance.
The equation for the model is:
Variables
In this model we have the following:
y = the dependent variable (i.e., what you measure as the signal)
x = the independent variable (i.e., what you control, such as dose, concentration, etc.)
Coefficients of the Fit
m = the slope of the fitted line
c = the intercept of the dependent axis
The rearranged equation to solve x is:
The goal is to determine values of m and c that minimize the sum of squares (SS) of the
residuals between the observed values (i.e., your data) and the predicted values (i.e., the fitted
curve). As some observed values will likely be above the fitted curve and some below, you will
get positive and negative residuals. SS is used to square each of the residuals, which render all
the values positive, then sum them. The smaller the SS, the closer the observed values are to
the predicted, the better the model predicts your data.
Fitted plot may show the nonlinear character of data. In this case you may and are advised to
consider setting other methods.
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Goodness of Fit Measures
R2, aR2, P, SE, F are calculated when each fit method is used. These measures are indicators as
to how position the data fits the curve fit model.
The following table describes these measures and how they can be used to assess the fit.
Name
Description
R² is 1 minus the ratio of the sum of the squares of the residuals divided by
the sum of the squares of the differences between Y fit and the mean Y value).
This will equal 1 for a perfect fit and tend toward 0 for a bad fit.
R2
In other words, R² is the ratio of variation that is explained by the curve-fitting
model to the total variation in the model.
In most situations, irreducible errors in measurement will prevent the model
from explaining all the variation. Models using a larger set of factors may
produce an R² value that is closer to 1. However, it may be that the additional
factors are essentially modeling noise.
aR² is the adjusted R² value.
aR2
aR² is R² adjusted downward to compensate for over-fitting. The larger the
number of independent variables is (compared to the number of
observations), the lower the adjusted R² value will be. When using curvefitting models with a larger number of independent variables, the additional
variables may be simply modeling noise.
F
F is the F-statistic. F is the ratio of the variance explained by the curve-fitting
model to the residual variance.
P
P is the P-value. P is the significance of the model as a probability. It is the Pvalue of F for the curve-fitting model. This compares the variance attributed to
the model with the variance of the residual.
SE
SE is the standard error. The standard error is the root-mean square of the
residuals.
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Best Fit
The Best Fit feature will automatically find the most appropriate fit for your data. The feature
processes your data with multiple curve fits and multiple weighting methods and
automatically picks the best performing fit.
When working with multiplex data and best fit, a best fit search is performed on each
analyte. Thus, the results for each analyte in an assay may use different fit methods
— the best for each analyte.
Which Fits?
The default configuration will compare the 4PL and 5PL fit methods each with unweighted
and various weighting models. These fit methods are the most widely used fit methods for
dose-response curves.
Beyond this default configuration, you can select which fit methods and which weighting
models to compare. It is also possible to define your own curve-fit equation.
What is Best?
What is deemed to be the best fit can be customized. For example, you might consider the fit
with the highest R² to be the best. You also might want to consider the accuracy of the curve
at certain regions of the fit (e.g., at the low or high end of your standards data).
To achieve this flexibility, the system uses a scoring method to quantify how good each fit is.
The fit with the highest score is considered to be the best fit.
For convenience, the following preset scoring methods are available for selection:
l R²
l R² with Low-End Accuracy
l R² with High-End Accuracy
l R² with Low-End and High-End Accuracy
l 1/Standard-Error
l 1/P-Value
Beyond these presets, you can define your own scoring methods to evaluate and compare
each attempted fit.
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Scoring Method Expressions
This section describes how to define your own scoring method expressions to evaluate a fit:
On completion of each fit, the fit is scored using a scoring method equation. The scores of
each fit are compared, and the fit with the highest score is considered to be the best. Thus, the
score method equation should quantify how good the fit is.
The scoring method equation can reference the goodness measures determined from the fit:
Goodness Measures
Name
Description
R² is 1 minus the ratio of the sum of the squares of the residuals divided by
the sum of the squares of the differences between Y fit and the mean Y value).
This will equal 1 for a perfect fit and tend toward 0 for a bad fit.
R2
In other words, R² is the ratio of variation that is explained by the curve-fitting
model to the total variation in the model.
In most situations, irreducible errors in measurement will prevent the model
from explaining all the variation. Models using a larger set of factors may
produce an R² value that is closer to 1. However, it may be that the additional
factors are essentially modeling noise.
aR² is the adjusted R² value.
aR2
aR² is R² adjusted downward to compensate for over-fitting. The larger the
number of independent variables is (compared to the number of
observations), the lower the adjusted R² value will be. When using curvefitting models with a larger number of independent variables, the additional
variables may be simply modeling noise.
F
F is the F-statistic. F is the ratio of the variance explained by the curve-fitting
model to the residual variance.
P
P is the P-value. P is the significance of the model as a probability. It is the Pvalue of F for the curve-fitting model. This compares the variance attributed to
the model with the variance of the residual.
SE
SE is the standard error. The standard error is the root-mean square of the
residuals.
Accuracy Functions
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In addition to the Goodness Measures, various accuracy functions are available to assess the
accuracy of the fit at various points. These accuracy functions enable a comparison to be made
between the expected value (the known x of the calibrator) and its actual value calculated
from the curve.
The calibration data set is the known concentration values of the standards (x) plotted against
their measurements (y). Each curve is fitted to the calibration data set.
To measure the accuracy of the curve at a particular data point, the x value of the known
concentration (expected) is compared against the x value at this point computed from the
curve (actual).
% Recovery or % Accuracy is simply:
100 * Actual / Expected
To simplify combining accuracy functions together and with other measures (such as R²), the
accuracy functions return a value between 0 and 1 (where 1 is perfectly accurate).
Function Name
Description
AccuracyRatio
Calculates the accuracy ratio of the expected value and its actual
value. This is similar to % recovery; however, here a value
between 0 and 1 where 1 is perfect accuracy (i.e., 100% recovery). A % Recovery of 120% or 80% both equal an Accuracy
Ratio of 0.8. With AccuracyRatio, if the actual value differs from
the expected by a factor of 2 or more, the accuracy ratio is 0.
Calculates an accuracy factor of the expected value and its actual
value. This is computed using % Recovery and a Gaussian
function such that:
AccuracyFactor
% Recovery
Accuracy Factor
100
1
0
0.5
200
0.5
<0%
Value between 0.5 and 0
>200%
Value between 0.5 and 0
This function avoids reporting a value of 0 for scenarios where
the % Recovery is <0% and >200%.
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Function Name
Description
Diff
The absolute difference between the expected value, and is the
actual value.
Accuracy Function Forms
Each accuracy function comes in three forms:
l At Specified X Point
l From Low end (ends with Low)
l From High end (ends with High)
The following table provides example expressions calling accuracy functions and their result:
Expression
Result
AccuracyRatio (0.125)
The accuracy ratio for calibrator where x = 0.125
AccuracyRatioLow (1)
The accuracy ratio for the calibrator with the lowest x value
AccuracyRatioLow (2)
The accuracy ratio for the calibrator with the second-lowest x
value
AccuracyRatioHigh (1)
The accuracy ratio for the calibrator with the highest x value
AccuracyRatioHigh (2)
The accuracy ratio for the calibrator with the second-highest x
value
Combining Measures
A scoring method can reference various goodness measures and accuracy calculations.
Remember that the fit with the highest score is considered to be the best. Therefore, if you
plan to consider multiple measures, it is recommended that each measure is a value between
0 and 1 and that the various measures are multiplied together.
Score Method Preset Expressions
To illustrate example scoring methods, the following table lists the expressions used for each
of the preset scoring methods:
Preset
Expression
R²
R²
R² with Low-End Accuracy
R²*AccuracyFactorLow(1)
R² with High-End Accuracy
R²*AccuracyFactorHigh(1)
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Preset
Expression
R² with Low-End and High-End Accuracy R²*AccuracyFactorLow(1)*AccuracyFactorHigh(1)
1/Standard-Error
1/SE
1/P-Value
1/P
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Standard Curve Fit Advanced Configuration
Standard Curve Fit Custom Flag Labels
When computing concentration values from a curve, there might be samples outside certain
ranges that should be flagged and reported with a custom label rather than as a result or
error.
The Standard Curve Fit Custom Flag Labels feature enables rules to be defined to specify
when a result should be flagged and reported using a label.
Example Uses
A typical use of this feature is to label results for standards where the results cannot or should
not be computed from the curve. For example, if a sample is outside the usable range of the
curve, then it might be useful to flag and label the result accordingly.
What is considered as the usable range of the curve is open to your own interpretation. For
example, the use of the curve could be limited by:
l the determined ULoQ and LLoQ values
l the mathematical limits of the curve
l your own defined limits (defined as absolute values or using expressions with limits
computed from the curve fit)
The Standard Curve Fit Custom Flag Label is an advanced option only available through XML
configuration. This feature is related to the Custom Flag Label feature, in this case Standard
Curve Fit Custom Flag Labels have higher precedence and override any other defined labels.
You may configure custom flagging rules in the Standard Curve Fit transform based on the
transform’s input or output data (calculated from the curve). If any of these rules are satisfied
then result is flagged with a specified label applied to it.
The rules are defined using expressions written in terms of x to define when a result should
be flagged.
The feature only applies to data points specifically flagged by the transform itself. If
an input point is already flagged, then any existing custom label (or the absence of
the custom label) is copied to the output.
Evaluation and Results
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You can define any number of Custom Flag Label rules in any order. If multiple labels are
configured, only the first label condition to be evaluated as true is used (in the order they
appear in the XML). These specified rules take precedence over the default flagging settings or
optionally specified CurveError. Therefore, if any of the rules with Type="Input" are
evaluated as true, then these will be used in place of any curve error.
For multiple plate assays, the associated curve for the position’s plate is used.
If a flag condition is satisfied and a Custom Flag Label is used, then an entry is added to the
Calculation Log detailing this application. If there is any error in evaluating a Condition, the
details of this is added to the error log.
How to: Add Custom Flag Label Rules
Standard Curve Fit Custom Flag Labels are configured using XML. To add flagging rules to a
Standard Curve Fit transform configuration:
1. Select the XML tab in the Assay Properties panel (If the XML tab is not immediately
visible, press the << Show All Properties button to expand).
2. Locate the StandardCurveFit element.
3. Add a Flags element (as a child of the StandardCurveFit) and, optionally, a CurveError
element.
4. Add a child element Flag for each required flagging rule. Set the attributes (in
accordance with the following table).
5. Press Apply to save changes to the protocol.
CurveError Optional Setting
The optional CurveError setting is used to specify a default custom flag label to use if a value
cannot be computed from the curve (i.e., if the result is flagged on the input matrix or while
calculating results). It simply replaces the default flagged result in the report with specified
CurveError Label.
This is specified in the XML inside the Flags element with following syntax:
<Flags CurveError="My Custom Error Label" >
Flag Element Attributes
The following table lists the attributes of the Flag element that must be specified to define the
flag rule:
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The value of x depends on the expression type:
Type
l For Type="Input" x is the value of the input position.
l For Type="Output" x is the value computed for the output position.
The condition to evaluate (typically a conditional expression).
The flag condition expression can refer to:
Condition
l the value of each position using the reference x.
l the computed coefficients of the curve and goodness measures.
l Standard Curve Named Evaluations.
l sample values of the input matrix.
Label
If a flag rule is satisfied (i.e., the Condition is evaluated to true), then the
result of the transform for the position is flagged with the associated Label. If
you insert {x} in the Label, it inserts the value of x (e.g., {x} is Below LLD
might be reported as 0.25 is Below LLD).
Specifying Rules with XML
When entering conditions as XML there are certain symbols that must be encoded using
correct XML syntax. These are listed in the table below.
Operator
Description
XML Encoding
<
Less Than
&lt;
>
Greater Than
&gt;
&
Ampersand
&amp;
"
Double Quote
&quot;
'
Apostrophe/Single
Quote
&apos;
For example, if the condition is x < a then this would be encoded in XML using x &lt; a
Example Configuration
The following XML snippet shows a complete configuration of the StandardCurveFit element
with Flag rules. With this configuration, any sample whose measurement response is above or
below the computed asymptotes of the 4PL (the a and d coefficients of the fit) will have a label
associated with it accordingly.
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<StandardCurveFit StandardType="2" FitMethod="4PL"
Concentrations="0.125,0.25,0.5,1,2,4" XAxisTitle="Concentration" YAxisTitle="Raw"
FitToStandard="Average" CalculateFrom="Average">
<Flags CurveError="Could not calculate from curve" >
<Flag Type="Input" Condition="x &lt; min(a,d)" Label="Below Lower
Asymptote" />
<Flag Type="Input" Condition="x &gt; max(a,d)" Label="Above Lower
Asymptote" />
</Flags>
</StandardCurveFit>
Example Flag Conditions
The following table lists further example flag conditions.
XML Condition
Description
<Flag Type="Input" Condition="x &lt;
If the measurement response is less than
Blank1+3*sd(Blank1)" Label="{x} Below LLD" Blank1 + 3SD, then the result is flagged and
/>
reported with the Label.
<Flag Type="Input" Condition="x &lt; min
(a,d)" Label="Below Lower Asymptote" />
If the measurement response is less than the
lower value of the a and d coefficients (as
computed from the curve), then the result is
flagged and reported with the Label.
If the measurement response is less than
<Flag Type="Input" Condition="x &lt; 0.125"
0.125, then the result is flagged and reported
Label="OD &lt; 0.125" />
with the Label.
<Flag Type="Output" Condition="x &lt;
0.125" Label="Conc. &lt; 0.125" />
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If the computed concentration is less than
0.125, then the result is flagged and reported
with the Label.
243
Standard Curve Fit Evaluations
In addition to computing concentrations, ULOQ, and LLOQ from the curve, you can evaluate
expressions that:
l Calculate X or Y values on the curve (using interpolation/extrapolation)
l Reference the coefficients and goodness measures of the curve
l Can be referred to as a variable in other evaluations or Standard Curve Fit custom flag
labels
l Can be referenced as the Transform Output Variables
This capability is useful for a range of scenarios, including calculating and reporting limits of
detection and quantification.
How to: Add Evaluations to a Standard Curve Fit Transform Configuration
1. Select the XML tab in the Assay Properties panel (If the XML tab is not immediately
visible, press the << Show All Properties button to expand).
2. Locate the StandardCurveFit element.
3. Add an Evaluations element (as a child of the StandardCurveFit).
4. Add a child element Evaluation element for each required evaluation. Set the
evaluation attributes in accordance with the table below.
5. Press Apply to save changes to the protocol.
Evaluation Attributes and Syntax
The expression to evaluate. It can refer to:
l the computed coefficients of the curve, Transform Output Variables and
goodness measures.
Expression
l any defined above in the XML Standard Curve Fit Evaluation with Name
attribute specified.
l sample values of the input matrix.
Since the result is a single numerical value, the expression cannot
refer to x or y variables.
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This setting describes how the expression is treated in report:
l CalcX – Calculates the X value from the fit line using Expression as Y
value. The resulting values are reported using numerical formatting of
input matrix.
Type
l CalcY – Calculates the Y value from the fit line using Expression as X
value. The resulting values are reported using standard numerical
formatting.
l Parameter – Outputs the computed Expression value.
Description
Optionally added textual description of the expression. If this is not specified,
the Expression itself is used in report.
Name
Optional element that defines a name for evaluation. If it is specified, the
Name can be used as a variable for other XML configurations (as described
below).
<Settings>
<StandardCurveFitStandardType="2" FitMethod="4PL" Concentrations="0.25,0.5,1,2,4,8"
XAxisTitle="Concentration" YAxisTitle="OD" FitToStandard="Average"
CalculateFrom="Average">
<Evaluations>
<EvaluationType="Type" Expression="Expression"
Description="Description"Name="Name" />
</Evaluations>
</StandardCurveFit>
</Settings>
Named Evaluations
If the Name attribute is set, then the evaluation can be referred by this Name as a variable in:
l Context variables in evaluations that are defined in XML downward to it
l Standard Curve Flag Label conditions
l Transform Output Variables
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The LLOQ and ULOQ are computed automatically and can be used as an evaluation
and flag variables too.
Configuring Standard Curve Evaluations
Evaluations are configured using XML and are evaluated using the plate for the associated
container. This means that Blank1 always refers to the first blank sample group on the
container. Further details are described in the Multiple Plate Standard Curve Fit Analysis topic.
For example, the LLD is often computed as the concentration (X) at the average of the blank
replicates + 3 * standard deviation of the blank replicates. This can be computed as an
expression that calculates X from the curve where Y = Blank + 3 * sd(Blank).
The LLOQ and ULOQ calculations are determined automatically and equal a concentration
value (i.e., one of the values defined in Concentrations).
If you create named evaluation LLOQ or ULOQ, then it replaces the default LLOQ and ULOQ
calculation, e.g.:
<Evaluations>
<EvaluationType="CalcY" Expression="Control1 + (10 * sd(Control1))"
Description="My LLOQ"Name="LLOQ" />
</Evaluations>
Results and Report
The LLOQ and ULOQ are the first entries, and any further evaluations are added below them
in the table.
The Evaluation data is displayed below the interactive chart and in the report as a simple
two-column table. The first column contains Description if specified or Expression if it was
not. The second column contains the numerical value of the evaluated result or any
syntax/computation error in red. In other words, report syntax errors in a table (in same way
as the Evaluation feature).
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Expressions
Overview
Expressions are available for use throughout MyAssays Desktop to provide flexibility in dataanalysis design and configuration.
An expression is simply an operation to be computed. In the simplest case, an expression can
refer to a specific numerical value, measurement, or result. For example, the
expression Control1 refers to the average of the Control1 sample replicates (that are not
flagged).
When writing expressions that refer to specific samples, in general, it is preferable to
refer to a sample group, such as Control1, rather than specific layout positions (such
as A1). With this approach, if your sample layout changes (perhaps you run the assay
with a different sample layout), then you will not need to update your expressions.
An expression might perform mathematical or statistical operations on these values. For
example, the expression pcv(Control1) computes the %CV of the Control1 replicates.
An expression can be applied at various levels of the data analysis to define and compute
various results. For example, matrix calculations, quantification limits, validation rules, values
from fitted curves, and best-fit scoring methods (to decide on the best fit) can all be defined
using expressions.
Wherever a numerical value can be entered as a settings parameter, an expression can usually
be provided to compute that parameter from other sources.
The Expressions system has been designed to:
Provide custom analysis using a familiar mathematical syntax (similar to Excel)
Provide an easy way to reference sample data
Take into account flagged data
Expressions can refer to measurements or calculated results from specific layout positions
(e.g., A1), sample groups (e.g., Control1), variables, and also the results of other expressions.
Culture Settings
When providing numerical values or expressions through the user interface, enter numerical
values and lists using your system's regional settings. There are two settings that can vary
between culture settings:
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l Decimal separator – Typically a period (.) on non-European systems and a comma (,) on
typical European system
l List separator – Typically a comma (,) on non-European systems and a semi-colon (;) on
typical European system
For example, depending on your system's culture settings, an example expression could be
entered in two forms:
mean(1.5,2.5)
mean(1,5;2,5)
For advanced users working with data entered directly into the XML editor, it is
necessary to follow XML conventions and provide data using invariant settings (that
is, period decimal separator for numeric values and a comma list separator).
Syntax and Context
The syntax of expressions is consistent throughout MyAssays Desktop. However, the
application of an expression depends on the context it is being used in. For example,
expressions are used in the configuration of transforms, validations, and evaluations. In each
case the application of the expression is slightly different.
Where an expression is used as a parameter to another setting, you can provide an expression
that can be evaluated to a numerical result, such as Control1 * 10.
There are several situations where an expression is entered in terms of x (where x refers to an
input value that will be substituted with a value for each evaluation of the expression). For
example, the Expression by Matrix transform expects an expression in terms of x. In this
context, you can provide an expression such as x-Blank1 to subtract the mean of Blank1
from all samples.
Where an expression is entered in terms of x, the expression is evaluated multiple times with x
being substituted with different values. In the case of the Expression by Matrix transform, the
expression is applied to every position on the input matrix to calculate the result of the output
matrix.
There are also scenarios where a Boolean expression is expected. Validations are defined
using a Boolean expression to define rules that must be satisfied for the assay to be
considered valid.
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For example, a validation rule can be defined with the expression Standard1<Standard2 to
check that the standard measurements are in the expected order. Similarly, a validation
expression x < 15 could be used along with %CV data to validate that all samples have a %CV
of less than 15.
A typical expression comprises of:
l operations, (e.g., +, *, /, ^)
l operands (e.g., A1, 1.23, x, Control1, TOV1.a)
l functions (e.g. pcv, sd, median, floor)
The following table lists some examples of expressions you may use in your assay.
Example Expressions
All expression examples provided here use a period decimal separator(.) and a
comma list separator(,). Use local settings of your system when entering expressions
on your own system.
Expression
Evaluated to
Unknown1
The average of the replicates of Unknown1 (that are not
flagged).
Unknown
The average of all Unknown samples (that are not flagged).
A1
The data for position A1.
A1+A2
The sum of the data at positions A1 and A2.
mean (A1,A2,A3)
The average of the data at positions A1, A2, and A3.
pcv(Unknown1)
The percentage coefficient of variation of Unknown1.
Blank1+(3*sd(Blank1))
The average of the sum of Blank1 plus 3 times the standard
deviation of Blank1 (this is often used to compute LLD).
pcv
The percentage coefficient of variation of all Unknown1 and
([Unknown1,Unknown2]) Unknown2 replicates.
median (Standard1)
The median of the replicates of Standard1 (that are not
flagged).
x-Blank1
The value of variable x minus the average of Blank1 position.
x > 15
A Boolean expression that would evaluate to 1 (true) if the
value of variable x is greater than 15; otherwise it evaluates to
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Expression
Evaluated to
0 (false).
Ifthenelse (x>15, 15, x)
If the value of x is greater than 15, then the result is 15;
otherwise the result is x.
x-y
The difference between x and y. Here x and y are the values of
corresponding positions from two matrices of data (as used in
the Expression by Matrix (Dual) transform).
median(Unknown.2.All)
The median of all Unknown replicates on matrix 2 across all
containers.
Unknown1.2.3
The average of Unknown1 sample group on the 2nd matrix of
3rd container.
TOV1.R2
The R2 value of the curve fit as used in the first transform
(referenced using a Transform Output Variable).
Operators
Unary Arithmetic Operator
Operator
Operation
-
Minus Sign
Examples
-1.23
-x
Binary Arithmetic Operators
Operator
Operation
Example
2+1
+
Addition
x+y
x+1
2-1
-
Subtraction
x-y
x-1
2*3
*
Multiplication
x*y
y*2
/
Division
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Operator
Operation
Example
x/y
x/2
2^3
^
Raising to a power
x^y
x^2
Binary Logical Operators
The numerical evaluation of 1 for true and 0 for false. Thus, 1>0 =1.
Operator
Operation
Examples
1<2
<
Less Than
x<y
x<2
2>1
>
Greater Than
x>y
x>2
1=1
=
Equal To
x=y
x=2
3<=9
3<=3
<=
Less Than Or Equal
x<=y
x<=x
x<=3
9>=3
3>=3
>=
Greater Than Or
Equal
y>=x
x>=x
x>=9
<>
Not Equal
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Operator
Operation
Examples
x<>y
x<>3
Boolean Operations
The Boolean operations (and, or, not) are implemented as Conditional Functions.
Operands
Numbers
Number operands are simply numbers (e.g., 1.23, -100, 14).
Ensure that all number values entered into the user interface are provided using the
local settings of your system.
Variables
The x and y variables refer to the value of each position on the associated matrix. All
references to these variables in expressions are equated to the associated values.
For example, in the Expression by Matrix transform, you may enter the x*100 expression,
where the x variable will refer to the positions of the input matrix. In the resulting matrix, all
positions will be multiplied by 100. In Expression by Matrix (Dual), the x and y variables are
set to the position values of the first and second matrices. Thus, when you enter an expression
x+y, each position on the resulting matrix will be the sum of input matrices values.
Variable
Relevant For
None
Preconfigured Protocols, Certain Endpoint: Auto Flag, Average, Matrix Difference, Median, Percentage, Percentage Coefficient of Variation, Replicate
Outlier Removal, Standard Curve Fit, Standard Deviation, Variance
x
Certain Endpoint: Blank Correction, Dilution Curves, Dilution Factors, Expression by Matrix, Expression by Position, Factor, XY Blank Correction
x, y
Expression by Matrix (Dual), XY Expression (Dual)
y
XY Expression
Fit Transforms Context Parameters
XY Reduction Fit Parameter and Standard Curve Fit transforms calculate the context
parameters of the fit. The former refers to the parameters in configuration settings, and the
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latter uses them in flagging conditions and/or additional evaluations. These fit transform
context parameters are:
l Goodness of Fit Measures calculated from the fit.
Name
Expression Syntax
R2
r2
aR2
ar2
F
f
P
p
SE
se
l Coefficients of the Fit – These vary depending on the selected fit method and are
specified within documentation of these methods.
Standard Curve Fit also produces additional values (such as quantification limits) and any
defined named evaluations. These can be optionally used for further evaluations of custom
flagging within this transform.
Fit Transforms Context Parameters apply within fit transforms only and cannot be
used in general configuration of the assay (e.g., validation or evaluation tables) or
other transforms.
Transform Output Variables
Any transform evaluated after the transform that produces the output variable can refer to it.
This applies only where the later transform supports parameters with expression.
Layout References
When an evaluation depends on and is associated with certain layout, the expression can
include references to positions, sample groups, and/or types defined on the layout. These are
defined with case-insensitive input (i.e., uppercase and lowercase letters in expressions are
treated as being the same).
For example, Unknown1 is the same as unknown1, UNKNOWN1, and UnKnOWN1.
General information on referencing the sample layout and its components is provided in the
Layout References in Expressions.
Position References
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A position ID refers to a specific position on the layout (e.g., A1, H12). To learn more, see
Positioning on the Layout.
The available dimensions depend on the layout read. A position on a particular matrix and
plate can be specified with a period, as described in sample references in multiple container
assays.
When writing expressions, it is better to refer to sample groups such as Blank1
rather than specific positions. If you have to modify your layout (perhaps because of
pipetting error), you do not need to update your expressions as the group reference
will refer to the modified layout.
Sample References
Sample operand can contain a reference to:
l Sample group – It can refer to any group of any type defined on the layout. To use this
operand in the expression, type in the sample type name (e.g., Unknown, Blank,
Standard) and sample group number. Example: Unknown1 refers to all non-flagged
Unknown1 sample group positions on the layout.
l Sample type – If no group number is specified, then it refers to all positions of that type
on the layout (e.g., all Unknowns, Blanks, Standards). Example: Unknown refers to all
non-flagged data points of Unknown type on the layout.
The simple sample reference usually refers to the average of non-flagged positions of the
type/group. Only for group-enabled functions does it refer to the list of values of each
non-flagged position that belong to the sample reference. Example: median (Unknown1)
calculates the median of all defined on the layout non-flagged Unknown1 position values.
If a sample reference is enclosed in square brackets (e.g., [Unknown1]), it refers to the list of
values of each non-flagged position. So [Unknown1] is actually expanded to the list of
results for the non-flagged members of Unknown1.
Sample References in Multiple Container Assays
To simplify evaluation and avoid the need to write a different expression for each container,
expressions use local group numbering. Thus, the simple Unknown1 reference always refers
to the first Unknown sample group on the container being evaluated.
You can refer to a specific position, group, or type defined on specific layout plate with the
syntax described below.
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Reference
Refers To
A1.2
A1 position for 2nd container of data
A1.2.3
A1 position for the 2nd matrix of 3rd container of data
A1..3
A1 position for 3rd container of data
A1.2.All
The average of all A1 positions on matrix 2 across all containers
Unknown1.2
Unknown1 sample group for 2nd container of data
Unknown.2.All
The average of all Unknown replicates on matrix 2 across all containers
Unknown1.2.All The average of all Unknown1 replicates on matrix 2 across all containers
Unknown1.2.3
Unknown1 sample group for the 2nd matrix of 3rd container of data
Unknown1..3
Unknown1 sample data for 3rd container of data
Matrix Position References
This operand comes in handy for accessing the data on the specified matrix at the position the
expression is being evaluated for. It is relevant for endpoint data only; however, it is useful for
configuring how the XY (e.g., kinetic or spectral) data is treated in analysis. To see further
detains and configuration examples, please refer to the Matrix Position Reference subsection.
Matrix Position Reference
A Matrix Position Reference is a convenient way to refer to data in a specific matrix. For
example, the expression:
#1 - #2
can be used to compute the differences between the values on the first and second matrix (for
all positions on the layout).
A Matrix Position Reference refers to data at the associated position on a specific
matrix (and optionally on a specific container/plate/analyte).
When is a Matrix Position Reference Available?
Matrix Position References are available where the expression being evaluated is associated
with a position. Examples of transforms with position associated expressions are:
l Expression by Matrix
l Expression by Matrix (Dual)
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l XY Expression
l XY Expression (Dual)
l XY Reduction X at Y
l XY Reduction Y at X
Also, the Range parameters (X Minimum and X Maximum) of XY transforms are position
associated.
In these cases, the expression is evaluated at each and every position. The matrix position
reference provides a convenient method for accessing the data on the specified matrix at the
position the expression is being evaluated for.
Syntax
A Matrix Position Reference is available in two forms:
l With Matrix Number
l With Matrix Number and Container number (e.g., plate/multiplex analyte)
The matrix number is the 1-based number of the matrix. These are listed in the Matrices tab.
Example References
Matrix Position Reference
Refers To
#1
Matrix 1
#1.2
Matrix 1 Container 2
Matrix Position References are only applicable to endpoint matrices. If you
mistakenly refer to a matrix that is not endpoint (e.g., XY, kinetic, or spectral) there
will be an error evaluating the expression.
XY Data and Matrix Position Reference Examples
When working with XY data (e.g., kinetic or spectral), it is useful to be able to reference
calculation results from the previous matrices for further analyses.
Example 1
For a kinetic data analysis, it might be useful to perform calculations only on data after a peak
point has occurred. To achieve this, the first step would be to add an XY Reduction Peak
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transform that will compute the peak for each plot. To do this, use the default settings of this
transform and set the result to be the X value at the peak for each position.
At this point we have two matrices, one Raw and one Calculated. The matrices (and their
numbers) can be found under the Assay Properties | Matrices tab, e.g.,
The next step would be to add another transform that uses the computed value of each
sample. For example, add the XY Reduction Total transform. This simply calculates the total of
the measurements. To configure the transform to only consider measurements from the peak
set the X Minimum range option to:
#2
I.e.:
With this setting, the transforms will compute the total of the measurements for each sample
but only from the peak point, e.g.:
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Example 2
In another kinetic example, the time taken to reach half peak can be computed. In this case:
1. The XY Reduction Peak can be used to compute the peak, this time with the result being
set to the Y value of the peak.
2. The Expression by Matrix transform is used to compute the Y value at half of the peak.
3. The XY Reduction X at Y transform can be used to calculate the time (X) when the halfpeak (Y) is reached. In this case the Matrix Position Reference #3 is used to reference
matrix 3 (the half-peak) as the Cursor for the calculation.
I.e.:
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Functions
This section provides reference information for various functions available to perform
calculations on data.
Syntax
These take the form of a function name followed by a comma-separated list of values in
brackets. The function data can refer to any operand type.
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1. If the function refers to a single operand of any type:
function (operand)
2. If the function refers to a list of operands:
l If operands do NOT include sample references:
function (operand1,operand2, ..., operandN)
l If operands include ONLY sample references:
function ([operand1,operand2, ..., operandN])
l If operands include any operand type AND sample references:
function ([samplereference1,samplereference2, ..., samplereferenceN],
operand1, operand2, ..., operandN)
3. If the function requires vector operands:
l function ({list of Ys}, {list of Xs})
l function({(samplereference1),(samplereference2), ..., (samplereferenceN)},
{operand1,operand2, ..., operandN})
Group-Enabled Functions
This section describes functions that operate on list of values with an open-ended length.
Where a sample reference is used as a parameter to a group-enabled function, any sample
group/type reference inside a Group-Enabled Function’s parameter list refers to the nonflagged values of that group/type.
These functions are specified with the following syntaxes:
l function (Sample type or group reference) – If there is 1 input sample group/type
reference
l function ([Sample type or group reference, Sample type or group reference, ... ,
Sample type or group reference]) – If there are 2 or more input sample group/type
references.
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When there are 2 or more sample group/type references inside a parameter list,
insert each group reference inside square brackets (e.g., pcv([Standard1,
Unknown2]) to access the values of non-flagged data points.
The following table lists group-enabled functions:
Name
Description
Minimum
number of Examples
parameters
Unknown1
average The same as mean.
count Counts the number of non-flagged values.
1
1
average
([Unknown,
Blank])
count
(Unknown)
mean
(Unknown1)
mean
Calculates the mean of the non-flagged positions.
Calculates the median of a finite list of non-flagged
positions. If there is an even number of arguments
median
in the set, then median calculates the average of the
two positions in the middle.
pcv
sd
Calculates the percentage coefficient of variance
(%CV) of a finite list of non-flagged positions.
Calculates the standard deviation of a finite list of
non-flagged positions. The standard deviation is a
measure of how widely values are dispersed from
the average value (the mean).
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mean
([Unknown,
Blank])
median
(Unknown1)
1
median
([Standard2,
Unknown4])
pcv
(Unknown)
2
pcv
([Unknown1,
Unknown1.2])
sd
(Standard2)
2
sd
([Unknown1,
261
Name
Description
Minimum
number of Examples
parameters
Unknown3])
se
Calculates the standard error of a finite list of nonflagged positions.
se
(Unknown1)
2
se
([Unknown,
Control])
sum
(Unknown)
sum
Returns the total of all items in the finite list of nonflagged positions.
var
Calculates the variance of a finite list of non-flagged
positions. Variance is a measurement of the spread
between positions in a data set. The variance
measures how far each number in the set is from
the mean.
1
sum
([Unknown1,
Unknown1.2,
Unknown1.3])
var
(Unknown4)
2
var
([Unknown1,
Unknown1.2])
Example
Let's assume that we have some endpoint measurement data for a 12x8 microplate stored in
the Raw matrix. In this example we would like to find medium value for Unknown1 and
Unknown2 positions.
The example uses a layout with 16 Unknown sample groups, where Unknown1 at A1, A2
A3, A4, A5, and A6, Unknown2 at A7, A8, A9, A10, A11, A12, etc.). I.e.:
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Unknown
Here is some example raw data displayed to 3 decimal places:
Raw
1
2
3
4
5
6
7
8
9
10
11
12
A 0.364 0.323 2.105 2.124 1.213 1.226 1.159 1.190 0.110 0.116 1.463 1.481
B 0.455 0.441 1.869 1.888 1.203 1.218 1.180 1.206 0.132 0.130 0.786 0.768
C 0.587 0.575 0.474 0.514 1.206 1.239 1.203 1.210 0.113 0.116 0.613 0.626
D 0.806 0.774 0.364 0.378 1.167 1.187 1.211 1.241 0.101 0.106 0.489 0.490
E 1.105 1.065 0.279 0.367 1.215 1.225 1.233 1.262 0.111 0.114 1.579 1.549
F 1.425 1.445 0.378 0.411 1.196 1.206 1.226 1.199 0.109 0.108 0.792 0.775
G 1.791 1.803 0.560 0.597 1.205 1.228 1.308 1.306 0.126 0.127 0.652 0.659
H 0.253 0.270 0.463 0.470 1.214 1.261 0.115 0.116 0.115 0.115 0.490 0.477
We use the expression:
median ([Unknown1, Unknown2])
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Unknown1 and Unknown2 refer to the lists of non-flagged data points for the Unknown1
and Unknown2 sample groups. From this list the median is calculated.
The result will be (also displayed here to 3 decimal places):
1.202
Conditional Functions
These functions require a specified number of input parameters. Where a sample reference is
used as an input parameter to a conditional function, it refers to the average of the nonflagged data points.
Boolean Logical Functions
Name
and
Description
Examples
Tests a number of input conditions and returns 1 if all of the conand(1,1)
ditions evaluate to true, or 0 otherwise.
or
Tests a number of input conditions and returns 1 if any of the
conditions evaluate to true, or 0 otherwise.
not
Returns a logical value that is the opposite of an input logical
value or expression (i.e., returns 0 if the supplied argument is
true and returns 1 if the supplied argument is false).
or (1,1)
or (x<5, x>10)
not(x>1)
Conditional Function
Name
Description
Examples
Requires 3 parameters:
1. The Boolean condition to evaluate (which will evaluate to
true or false).
ifthenelse
2. The result if the condition is true.
3. The result if the condition is false.
If the value of the first condition is true, then the result is the
second argument otherwise the third.
ifthenelse
(1=1,2,3) result
is 2
ifthenelse
(1=0,2,3) result
3
For example, with the reference:
IfThenElse(Unknown1 > 10, 10, Unknown1)
Unknown1 refers to the average of Unknown1 non-flagged positions. Thus, the expression
evaluation result is 10, if Unknown1 is greater than 10; otherwise the result is Unknown1.
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More functions with fixed parameter length are described in Layout Analysis Functions.
Calculation Functions
This section describes functions that calculate the result of mathematical operations on an
argument or a list of arguments. Calculation functions use general syntax for functions and
perform an operation on a list of 1 or more operands. The evaluation of these results in a
single numeric value.
The following table lists calculation functions.
Name
abs
Description
Returns the absolute value of argument. The
absolute value of a non-negative argument is the
argument itself. The absolute value of a negative
argument is -1 times the argument.
average Described in group-enabled functions.
Examples
abs(-1.23)=1.23
abs(1.23)=1.23
average(1,2)=1.5
average(-3,0.25,5)=0.75
Returns argument ({0}) rounded up, away from zero,
to the nearest multiple of significance ({1}). It
requires two input parameters with the following
syntax ceiling({0}, {1}), where:
ceiling
l {0} is the value you want to round off.
l {1} is the multiple to which you want to round.
ceiling(1.23, 0.05)=1.25
ceiling(-1.23, 0.05)=-1.2
Regardless of the sign of a {0}, a value is rounded up
when adjusted away from zero. If the {0} is an exact
multiple of {1}, no rounding occurs.
count
Described in group-enabled functions.
Returns e raised to the power of the given number.
Syntax: exp({0}) where:
exp
floor
{0} is the exponent applied to the base e. It accepts
only a single argument. For example, the system
accepts exp(A1+A2) and rejects exp(A1,A2).
count(1,2,3)=3
count(-2.44,2,5,98,6)=5
exp(2)=7.389056...
exp(-1)=0.367879...
Returns argument ({0}) rounded down, toward zero,
floor(1.23, 0.05)=1.2
to the nearest multiple of significance ({1}). It
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Name
Description
Examples
requires two input parameters with the following
syntax floor({0}, {1}), where:
l {0} is the value you want to round off.
l {1} is the multiple to which you want to round.
floor(-1.23, 0.05)=-1.25
Regardless of the sign of a {0}, a value is rounded
down when adjusted away from zero. If the {0} is an
exact multiple of {1}, no rounding occurs.
Calculates the point at which the least square's fit
line will intersect the Y axis by using existing x-values
and y-values. The intercept point is based on a bestfit regression line plotted through the known xvalues (independent set of arguments) and known yintercept({1,2},{3,2})=4
values (dependent set of arguments).
intercept({-2.44,2,5,98,6},
intercept
Requires a vector of arguments as
{0.1,0.2,0.3,0.4,0.5})=parameters.
12.152
The number of Y and X operands is required to be
the same and greater than 2. In other words,
{y1,y2,y3} and {x1,x2,x3}; {y1,y2, ... yN} and {x1, x2, ...
xN}.
ln
Returns the natural logarithm of an argument.
Natural logarithms are based on the constant e
(2.718281828459…). The argument is supposed to
be positive.
ln(10)=2.302585...
ln(1.23)=0.207014...
ln is the inverse of the exp function.
log
Calculates the logarithm of an argument to the base
that you specify. This uses the following syntax with
log(2,4)=0.5
two input parameters: log({0}, {1}) where:
log(1.23,2)=0.298658...
{0} is the positive real numerical value or expression
for which you want the logarithm.
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Name
Description
Examples
{1} is the base of the logarithm. If the base is
omitted, it is assumed to be 10.
log10
Returns the base-10 logarithm of a positive
argument.
log10(1.23)=0.089905...
log10(0.1)=-1
Returns the largest value in a set of values.
max
To determine the maximum of positions in
a sample group or sample type, type in the
argument it square brackets (i.e., max
([Unknown1])). Otherwise the reference is
treated as the average of positions.
median Described in group-enabled functions.
min
mod
max(1,2,3)=3
max(-2.44,2,5,98,6)=98
median(1,2,3,4)=2.5
median(-2.44,2,5,98,6)=5
Inversely to max function, returns the smallest num- min(1,2,3)=1
ber in a set of values. All requirements of the max
min(-2.44,2,5,98,6)=-2.44
apply to min too.
Returns the remainder after the argument is divided
by a divisor. The result has the same sign as the
divisor. Syntax: mod({0}, {1}) where:
mod(2,0.3)=0.2
{0} is the argument for which you want to find the
remainder.
mod(1.23,5)=1.23
{1} is the value by which you want to divide the {0}.
pow
Returns the result of a number raised to a power.
Syntax: power({0}, {1}) where:
power(2,3)=8
{0} is the base argument.
power(1.23,5)=2.815305...
{1} is the exponent to which the {0} is raised.
slope
Returns the slope of the linear regression line
slope({1,2},{3,2})=-1
through data points in Xs and Ys. The slope is the
vertical distance divided by the horizontal distance
slope({-2.44,2,5,98,6},
between any two points on the line, which is the rate {0.1,0.2,0.3,0.4,0.5})=112.88
of change along the regression line.
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Name
Description
Examples
Requires a vector of arguments as
parameters.
The number of Y and X operands is required to be
the same and greater than 2 (i.e., {y1,y2,y3} and
{x1,x2,x3}; {y1,y2, ... yN} and {x1, x2, ... xN}).
stdev(1,2,3)=1
stdev
The same as sd in group-enabled functions.
stdev(2.44,2,5,98,6)=42.772359...
sum(1,2,3)=6
sum
Described in group-enabled functions.
sum(2.44,2,5,98,6)=108.56
var(1,2,6)=7
var
Described in group-enabled functions.
var(2.44,2,5,98,6)=1829.47472
Layout Analysis Functions
Layout Analysis Functions are available only if the evaluation is associated with a layout.
Otherwise an error message displays.
Layout Evaluation Functions
Layout Evaluation functions are provided to enable sample group references to be more
flexibile.
The following table lists the layout evaluation functions:
Name
GroupValue()
Description
Example
Returns the value of the sample group with the
specified type and group number (The type name
should be specified in quotes).
GroupValue
("Unknown",1)
The GroupValue function can be used in conjunction with PosGroup() to obtain the
group of a specified type that matches the group number of the position being
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evaluated. For example, if you wanted to reference the Unknown group that has the
same number as the current position, use: GroupValue("Unknown", PosGroup())
Position Functions
Position Functions are only available when there is a position associated with the expression
being evaluated. They take no input parameters.
The following table lists the position functions:
Name
Description
Example
PosNum()
Returns the 1-based position number of each position,
which is assigned in accordance with position
PosNum()
numbering.
PosGroup()
Returns the 1-based group number of the sample
group at the position. For example, if the position
contains Unknown3, then the group is 3.
PosGroup()
Returns the sample type number associated with the
position.
PosType()
PosType()
PosReplicate()
Returns the 1-based replicate number of the specified
in the argument position. Replicate numbers are
assigned in accordance with position numbering.
PosReplicate
()
This function is based purely on the layout
and does not take flagging into account.
Layout Functions
These functions require an associated layout.
Layout Function Parameters
Type
Type can be specified as a sample type number or case-insensitive sample type name in
quotes, e.g.:
l LayoutNumGroups (3)
l LayoutNumGroups ("Blank")
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l LayoutNumGroups ("blank")
l LayoutNumGroups ("bLaNK")
"Unused" type is not supported.
ContainerNum
Where a layout function accepts the ContainerNum argument:
l If ContainerNum is 0 (or not specified), then the function will be evaluated for all
containers in the layout.
l If ContainerNum is specified (as a 1-based container number), then only the layout for
that container is considered.
The following table lists the layout functions:
Name
Description
Example
LayoutWidth()
The number
of columns in
the layout
(note, this is LayoutWidth()
same value
for all containers).
LayoutHeight()
The number
of rows in the
layout (note,
LayoutHeight()
this is same
value for all
containers).
LayoutPositions()
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The total
number of
positions
(note, this is
same value
for all
containers).
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270
Name
Description
Example
LayoutNumContainers()
The number
of containers LayoutNumContainers()
in the layout.
LayoutNumGroups(Type,
ContainerNum)
The number
of groups
with the
specified type.
Input
LayoutNumGroups(3, 1)
parameters:
Type,
ContainerNu
m.
LayoutNumPositions(Type,
GroupNum, ContainerNum)
The number
of positions
with the
specified type
and 1-based
group
number
LayoutNumPositions("standard",
(GroupNum). 3, 1)
Input
parameters:
GroupNum,
Type,
ContainerNu
m.
LayoutNumUsedPositions
(ContainerNum)
The number
of positions
that have a
type that is
not unused
(i.e., Type !=
1).
LayoutNumUsedPositions(2)
Lay-
Determines
Lay-
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Name
outNumGroupsAcrossContainers
(Type)
Description
Example
the number of
groups of the
specified Type
parameter on
each of the
containers. If
this is the
same number outNumGroupsAcrossContainers
on every
("Unknown")
container
(including 0),
then this
value is
returned.
Otherwise -1
is returned.
Integration with Readers
VICTOR Manager Integration with MyAssays Desktop
Overview
This topic describes how to configure the PerkinElmer VICTOR manager software to
automatically launch a MyAssays Desktop protocol for analysis of the measured data.
With this configuration in place, data analysis can be performed automatically as soon as
VICTOR measurements are completed with little or no user intervention is required. In
addition, results can be automatically saved/printed/exported as required to provide a
seamless integrated measurement and analysis solution.
Terminology
Between the separate VICTOR manager software and MyAssays Desktop software, there is
some overlap in terms used. Both systems use the term “protocol”, for clarity in this section
we refer to a VICTOR protocol as the VICTOR measurements protocol and a MyAssays
Desktop protocol as the MyAssays Desktop protocol.
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Integration Workflow
The general workflow is to:
1. Use the VICTOR manager software to create and configure the required measurement
settings (the VICTOR measurements protocol).
2. Export the measurement data to a TXT file.
3. Create a MyAssays Desktop protocol to perform the required analysis.
4. Configure the VICTOR measurements protocol to launch the MyAssays Desktop along
with the MyAssays Desktop protocol when the measurements are completed and
provide the path to the exported TXT file (using the command line).
The creation of a VICTOR measurement protocol and a MyAssays Desktop protocol are not
covered here. It is assumed that you already have these in place and are ready to integrate
them.
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MyAssays Analysis Launcher
MyAssays Desktop data analysis can be initiated from the command line. MyAssays Desktop
offers many options and settings for complete control over the data analysis process.
Writing command line arguments is typically a complicated error-prone process. To simplify
this task, MyAssays Desktop includes the MyAssays Analysis Launcher tool. This provides a
point-and-click user interface for building a command line to launch MyAssays Desktop
analysis. The resulting command line argument is displayed within the tool and can be easily
copied and pasted into other applications.
To get started, we recommend familiarising yourself with the MyAssays Analysis Launcher
tool. This can be found in MyAssays Desktop Explorer under the Tools tab. Select the Analysis
Launcher option to launch it.
Using Analysis Launcher
To use Analysis Launcher, select a MyAssays Desktop protocol file to launch and use the user
interface to configure the operations required.
If you have a compatible VICTOR TXT file to hand, use the Import Measurements option and
select the file.
If you would like the results to be calculated automatically, check the Calculate option.
Notice, in the lower part of the Analysis Launcher the tool that as settings are made the
command line argument is updated accordingly.
To launch the analysis press Launch.
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Example Integration
This section describes how to modify a VICTOR measurement protocol to launch MyAssays
Desktop:
1. Launch the PerkinElmer VICTOR Manager Explorer.
2. Select and open the VICTOR measurements protocol to edit.
You should see a window displaying the measurement configuration
settings, similar to this:
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3. Select the Outputs and Events tab
4. Check the File output setting
5. Select File type as Text
6. Specify the Filename as required (this could be made up of the various macros)
For example, in this configuration the file is output to the Documents folder
with a name created from the Protocol Name and Run ID:
With this setting in place, the results are exported to a text file when measurements are
completed. You can try this now (perhaps with an empty plate) to check that the settings are
expected.
You can then test importing this data into a MyAssays Desktop protocol “manually” by
launching the MyAssays Desktop protocol and using the Import Wizard to import the text
file.
The final part is to configure the VICTOR measurement protocol’s Assay end system
command setting. This is used to issue the command line.
1. Return to the VICTOR measurements protocol editor.
2. In the first edit box, enter the full path to the MyAssays Desktop Analysis exe, this is
typically:
C:\Program Files\MyAssays\MyAssays
Desktop\MyAssays.Desktop.Analysis.exe
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If you have installed MyAssays Desktop to another location, then you will
need to enter the correct path. If you are not, use the MyAssays Analysis
Launcher application and inspect the command line:
3. In the second edit box, enter the MyAssays Desktop protocol filename to use followed
by:
/import-raw:<OutputFileName>
This setting passes the filename generated by the VICTOR manager software
to MyAssays Desktop so that the new measurement data file can be
imported. The <OutputFileName> is a macro used by VICTOR manager
software to refer to the new measurement data file.
For example, to use the MyAssays Desktop protocol: My Endpoint 12x8
Protocol (1) in my documents folder I use:
"C:\Users\Darren Cook\Documents\MyAssays\Protocols\My
Endpoint 12x8 Protocol (1).assay-protocol" /importraw:<OutputFileName>
This would appear in the VICTOR measurements protocol editor as follows:
4. Save these changes to the VICTOR measurements protocol.
Similarly, if you are not sure, use the MyAssays Analysis Launcher application and
inspect the command line.
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With this setting in place, the results will be exported to a text file when
measurements are completed and then imported into MyAssays Desktop. You can
try this now (perhaps with an empty plate) to check that the integration is working.
With these basic settings, the calculations are automatically performed. This gives
the user an opportunity to review the settings and perhaps make some changes
(such as a different layout or marking outliers to exclude) before pressing the
Calculate button.
If you would like calculations to be performed automatically, add:
/calc
To the end of the command line argument in the VICTOR Assay end system
command, e.g.:
You can also add further command line arguments to automatically print, export, etc. Use the
Analysis Launcher tool or refer to the Command Line Arguments section for more
information.
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EnVision Integration
The EnVision manager software can output data in many possible export configurations.
To configure the EnVision manager software to output data in a format compatible with
MyAssays Desktop EnVision import scripts, use the default CSV Export format: Plate 2
(with well headers) option, e.g.:
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Command Line Arguments
MAA can be launched directly from the command line with optional specific arguments.
Syntax
>"C:\MyAssays Installation Root path\MyAssays
Desktop\MyAssays.Desktop.Analysis.exe" "C:\MyAssays Folder Root
path\MyAssays\Protocols\Protocol File Name.assay-protocol" /argument1
/argument2 etc.
Command line arguments are only relevant when the MAA is opened with an .assayprotocol file and can be specified in any order.
The following table lists optional command line arguments:
Argument
Description
Example
/calc
Performs specified calculations
automatically (if /import-raw is
used, then calculations are
performed using the imported raw
data).
/calc
/calc-log:{0}
Specifies a file to save calculation
progress log.
/calc-log:"Destination
file path"
/error-log:{0}
Specifies a file path to save
calculation errors log.
/error-log:"Destination
file path"
/exit
Closes MAA after calculations are
completed/printed.
/exit
Specifies the export options. {0} is
either:
l XLSX, PDF, XML (file export type).
/export-report:{0}:
l XSLT. Requires additional
{1}
arguments to specify output
filename extension and path to
XSLT 1.0 file.
/export-report:pdf
/exportreport:xslt1:csv:"XSLT
file path"
The exported file will be saved
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Argument
Description
Example
under \MyAssays\Reports\ folder.
The optional {1} specifies whether to :nolaunch
launch the exported file with its
default associated application. If it is /exportreport:xml:nolaunch
specified, the file will not launch.
Specifies a Plate IDs file to use in
data analysis.
/importcontainerids:{0}
/import-raw:{0}
This command-line
argument overrides
previously configured by
Import Wizard method.
/importcontainerids:"Data file
path"
Specifies the raw data file to be used /import-raw:"Raw data
for analysis.
file path"
Specifies a Sample IDs file to use in
data analysis.
/importsampleids:{0}
/import-tweaks:
{0}:{1)
This command-line
argument overrides
previously configured by
Import Wizard method.
/import-containerids:"Sample IDs
file path"
Specifies a protocol tweaks file that
/importdefines changes to the protocol
tweaks:"Protocol tweak
configuration.
file path"
l {0) - full path to the protocol
tweaks file.
The optional {1} specifies the save
setting. If it is specified, the protocol
/importfile will be saved with tweaks
tweaks:"Protocol tweak
automatically.
file path":saveprotocol
If the Save changes to
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Argument
Description
Example
protocol option is selected
(under File | Save in the
Analysis application),
then the changes will be
saved to the protocol
irrespective of this option
setting (e.g., when
calculations are
performed).
/noappwin
If this is specified, then the
application window is NOT
displayed.
/print
Automatically prints the report (if
the calculations succeeded) with the /print
default printer (if available).
/tab:{0}
Allows you to select the tab MAA
starts on. {0} is zero-based tab
number: Overview = 0;
Measurements =1; Microplate = 2; /tab:1
Sample IDs = 3; Results = 4. If this
option is not specified, then the first
tab will open.
/noappwin
The maximum command line argument is 255 characters. If the command line
argument exceeds this length, then a warning message is displayed and the program
is exited.
To use long command line arguments, store the command-line argument list in a
text file and launch MyAssays.Desktop.Analysis.exe, passing the text file containing
the command-line arguments:
MyAssays.Desktop.Analysis.exe @<CommandTxtFile>
Where <CommandTxtFile> is the name of the text file to read the command line
from.
Examples
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>"C:\MyAssays Installation Root path\MyAssays
Desktop\MyAssays.Desktop.Analysis.exe" "C:\MyAssays Folder Root
path\MyAssays\Protocols\Protocol File Name.assay-protocol" /import-raw:"Raw data
file path" /tab:4 /calc /export-report:xlsx:nolaunch
The selected assay protocol with specified raw data file will be computed. The MAA will start
with the Results tab and the XLSX report will be saved (but not automatically launched)
under \MyAssays\Reports\ folder.
>"C:\MyAssays Installation Root path\MyAssays
Desktop\MyAssays.Desktop.Analysis.exe" "C:\MyAssays Folder Root
path\MyAssays\Protocols\Protocol File Name.assay-protocol" /calc /print /noappwin
The selected assay protocol with specified raw data file will be computed and the results will
be printed out.
These settings can be configured using the Analysis Launcher.
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Tweak Files
This section provides information about the protocol tweaks feature. The protocol tweak files
are specified as optional command-line arguments.
This is an advanced feature and misuse of it could cause problems.
Overview
You may use tweak settings to define the data analysis parameters of a protocol
customization. This enables any of the assay protocol parameters to be specified by other
existing software such as instrument-control software or LIMS.
For example, a LIMS could initiate execution of data analysis and specify customizations (such
as standard concentrations or layout); the customisations are merged with the existing
protocol, the analysis performed, and results generated with data exported as required.
Tweak Syntax
A protocol-tweak.xml file is a XML with the following syntax:
<?xml version="1.0" encoding="utf-8"?>
<ProtocolTweaks>
<Tweak XPath=" Full assay setting to tweak path">Substitute data</Tweak>
</ProtocolTweaks>
You can insert any number of tweaks under <ProtocolTweaks> section.
Using the Feature
With your working protocol-tweak.xml file, use your instrument control or LIMS software to
insert relevant protocol-tweak.xml file's data and launch the protocol from the command line
when required. These tweak changes are not automatically saved to the assay file, and a
tweaked protocol appears as modified and not saved (with * in title).
If you save the protocol, the tweaks will be saved to the protocol file.
It is possible to launch, execute (generate reports), and exit MyAssays Desktop Pro analysis
without any user intervention required.
To achieve this, we recommend developing the protocol-tweak file as follows:
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1. Build or obtain an assay-protocol file that meets your data analysis requirements.
2. Identify which analysis parameters can be specified by your existing software.
3. Develop a protocol-tweak.xml file.
4. Test the protocol-tweak.xml file.
Any data analysis parameter can be modified.
How to: Add Protocol Tweaks
1. Launch the Command Prompt.
2. Enter the file path to the protocol tweak file as a parameter of the MAA command-line
arguments.
OR
Add a protocol tweaks file in the Analysis Launcher.
The system will perform certain checks on the incorporated tweaks to detect possible
configuration errors in common usage scenarios.
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Installing MyAssays Desktop Alongside
WorkOut
MyAssays Desktop can be installed and used alongside an existing WorkOut installation
(including WorkOut versions 1.x, 2.x, Plus, and Plus MMD). This is referred to as a Side-By-Side
installation.
Certain modules (relating to data import and analysis) are shared between the applications. If
you plan to use both WorkOut and MyAssays Desktop on the same system, then please follow
these points to avoid known conflict issues:
l Always install MyAssays Desktop after installing WorkOut (i.e., do not install, update,
patch, or repair a WorkOut installation after installing MyAssays Desktop).
l If WorkOut is uninstalled leaving a MyAssays Desktop installation, then it will be necessary
to also uninstall MyAssays Desktop and then reinstall MyAssays Desktop.
If MyAssays Desktop has been installed with a Side-By-Side WorkOut installation, then this
will be reported in the File | Information tab of MyAssays Desktop Explorer.
Known Issues
If the points above are not followed, then you may see the following problems with MyAssays
Desktop:
l Unable to import data
l Unable to produce a valid report (on data calculation)
To resolve these issues, please uninstall MyAssays Desktop and then reinstall MyAssays
Desktop to update the shared component files to the newest version.
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286
Referencing MyAssays Desktop
You can refer to MyAssays Desktop in your paper, thesis or publication.
To refer to the data analysis tool include the text:
“MyAssays Desktop Product Version from MyAssays Ltd., https://www.myassays.com"
To obtain current Product Version open Information screen of File tab in the
MADE. You can either click on the Copy icon or enter it manually to your
publication.
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287
Additional Software Licensing Notices
MyAssays Desktop utilizes the Newtonsoft.Json library. In accordance with the MIT license, the
following notice is included here in relation to the Newtonsoft.Json component:
The MIT License (MIT)
Copyright (c) 2007 James Newton-King
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in the
Software without restriction, including without limitation the rights to use, copy,
modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the
following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS," WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN
AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
MyAssays Desktop utilizes the MVVM Light Toolkit. In accordance with the MIT license, the
following notice is included here in relation to the MVVM Light Toolkit component:
The MIT License (MIT)
Copyright (c) 2009 - 2014 Laurent Bugnion
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in the
Software without restriction, including without limitation the rights to use, copy,
modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the
following conditions:
MyAssays Desktop User Guide
288
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN
AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
MyAssays Desktop utilizes the AvalonEdit. In accordance with the MIT license, the following
notice is included here in relation to the AvalonEdit component:
The MIT License (MIT)
Copyright (c) 2016 Daniel Grunwald
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in the
Software without restriction, including without limitation the rights to use, copy,
modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the
following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN
AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
MyAssays Desktop utilizes the Log4net library. In accordance with the Apache v2.0 license, the
following notice is included here in relation to the Log4net library:
Apache License
MyAssays Desktop User Guide
289
Version 2.0, January 2004
http://www.apache.org/licenses/
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END OF TERMS AND CONDITIONS
MyAssays Desktop utilizes the GongSolutions.WPF.DragDrop library. In accordance with the
BSD 3-Clause License, the following notice is included here in relation to theWPF.DragDrop
library:
BSD 3-Clause License
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Copyright (c) 2015-16, Jan Karger (Steven Kirk)
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
l Redistributions of source code must retain the above copyright notice, this list
of conditions and the following disclaimer.
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list of conditions and the following disclaimer in the documentation and/or
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l Neither the name of gong-wpf-dragdrop nor the names of its contributors
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