User Manual DeCyder Differential Analysis Software, Version 5.0

User Manual DeCyder Differential Analysis Software, Version 5.0
user manual
proteomics
DeCyder Differential Analysis
Software, Version 5.0
User Manual
um 18-1173-16 AA
Terms and Conditions of Sale
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subject to the terms and conditions of sale of the
company within the Amersham Biosciences group
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Patents and Licences
CyDye: 2-D Fluorescence Difference Gel
Electrophoresis (2-D DIGE) technology is covered by
US patent numbers US6,043,025, US6,048,982,
US6,127,134, and US6,426,190 and foreign
equivalents and exclusively licensed from Carnegie
Mellon University.
CyDye: this product or portions thereof is
manufactured under licence from Carnegie Mellon
University under US patent number US5,268,486 and
other patents pending.
The purchase of CyDye fluors includes a limited license
to use the CyDye fluors for internal research and
development, but not for any commercial purposes. A
license to use the CyDye fluors for commercial purposes
is subject to a separate license agreement with
Amersham Biosciences.
Amersham Biosciences has patent applications pending
relating to its DeCyder software technology, European
patent application number EP1,234,280.
©Amersham Biosciences AB 2003
-All rights reserved
Contents
1 Introduction to DeCyder Differential Analysis
Software
1.1 Introduction ............................................................................... 11
1.2 The DeCyder Differential Analysis Software User Manual ............ 11
1.3 Measuring differential protein abundance using
Ettan DIGE system ..................................................................... 12
1.4 Experimental Design using an Internal Standard ......................... 14
1.5 Integration of DeCyder Differential Analysis Software with
Ettan DIGE system experimental design ..................................... 17
1.6 Steps involved in Image Analysis using
DeCyder Differential Analysis Software ....................................... 19
1.7 Structure of DeCyder Differential Analysis Software ..................... 19
2 Computer requirements and installation
2.1 Computer requirements ............................................................. 23
2.2 Installation ................................................................................. 23
2.2.1
Installation with a previous version present ........................... 24
2.2.2
De Novo Installation ............................................................. 25
3 DIA (Differential In-gel Analysis) Module
3.1 Overview .................................................................................... 27
3.2 Creating and opening workspaces .............................................. 29
3.3 Spot detection and quantitation .................................................. 31
3.3.1
Detection ............................................................................. 31
3.3.2
Quantitation ......................................................................... 33
3.4 Viewing spot data ....................................................................... 34
3.4.1
Image View .......................................................................... 34
3.4.2
3-D View.............................................................................. 36
3.4.3
Table View ........................................................................... 38
3.4.4
Histogram View.................................................................... 40
3.5 Data analysis ............................................................................. 42
3.5.1
Spot exclusion ..................................................................... 43
3.5.2
Spot confirmation................................................................. 46
3.5.3
Protein of Interest ................................................................ 48
3.5.4
PTM assignment.................................................................. 48
3.6 Customizing display colors ......................................................... 48
3.7 Saving, exporting and printing .................................................... 49
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4 BVA (Biological Variation Analysis) Module
4.1 Overview ................................................................................... 51
4.1.1
Functionality........................................................................ 51
4.1.2
Structure ............................................................................. 52
4.2 Protein quantitation ................................................................... 53
4.3 Creating and opening workspaces ............................................. 53
4.3.1
Creating workspaces............................................................ 53
4.3.2
Opening workspaces ........................................................... 54
4.4 Viewing spot data ...................................................................... 54
4.4.1
Image View.......................................................................... 54
4.4.2
3-D View ............................................................................. 56
4.4.3
Table View........................................................................... 57
4.5 Defining spot map attributes ...................................................... 58
4.5.1
Function assignment ........................................................... 58
4.5.2
Group assignment ............................................................... 59
4.5.3
Comment assignment.......................................................... 61
4.5.4
Spot Map Table Mode ......................................................... 61
4.5.5
Spot Map Table properties ................................................... 62
4.6 Inter-gel matching ..................................................................... 63
4.6.1
Landmarking ....................................................................... 63
4.6.2
Matching............................................................................. 65
4.6.3
Match confirmation ............................................................. 65
4.6.4
Match Quality Metric ........................................................... 67
4.6.5
Spot Merging....................................................................... 68
4.6.6
Match Table ........................................................................ 70
4.6.7
Match Table properties........................................................ 70
4.7 Graphical representation ........................................................... 71
4.8 Protein statistics ........................................................................ 73
4.8.1
Opening the protein statistics dialog box .............................. 73
4.8.2
Defining groups ................................................................... 74
4.8.3
Overview of Statistical tests .................................................. 75
4.8.4
Independent and paired analyses ........................................ 76
4.8.5
Student’s T-test & Average ratio ........................................... 78
4.8.6
ANOVA................................................................................ 81
4.8.7
One-Way ANOVA ................................................................. 83
4.8.8
Two-Way ANOVA ................................................................. 85
4.8.9
Further statistical analyses ................................................... 90
4.9 Protein Filter ............................................................................. 90
4.10 Molecular weight (Mw) and isoelectric point (pI) ........................ 91
4.10.1 Entering Mw and pI of known proteins ................................. 91
4.11 User-defined protein labelling ................................................... 94
4.11.1 Confirmation........................................................................ 94
4.11.2 Name.................................................................................. 94
4.11.3 Comment ............................................................................ 94
4.11.4 Protein of interest ................................................................ 94
4.11.5 PTM .................................................................................... 94
4.12 Database linking ....................................................................... 94
4.12.1 Adding databases................................................................ 95
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4.13 Viewing protein data ................................................................. 98
4.13.1 Protein Table ....................................................................... 98
4.13.2 Appearance Table.............................................................. 101
4.13.3 Spot annotation.................................................................. 102
4.13.4 Customizing display colors ................................................. 103
4.13.5 Saving, exporting and printing ............................................ 104
5 Spot picking
5.1 Overview .................................................................................. 107
5.2 Spot detection on the preparative gel ....................................... 107
5.3 Identification of reference markers ........................................... 108
5.4 Identifying Proteins of Interest .................................................. 109
5.4.1
Identifying proteins for picking using the DIA module ......... 110
5.4.2
Identifying proteins of interest using the BVA module ......... 112
5.5 Assigning Spots for Picking ...................................................... 114
5.6 Editing Pick Locations .............................................................. 116
5.7 Generating a Pick List .............................................................. 119
6 Batch processor
6.1 Overview .................................................................................. 121
6.2 Batch list creation .................................................................... 121
6.2.1
Setting up the DIA Batch List ............................................. 122
6.2.2
Setting up the BVA Batch List ............................................ 125
6.3 Editing the batch list ................................................................ 126
6.3.1
DIA batch list ..................................................................... 126
6.3.2
BVA batch list .................................................................... 126
6.4 Saving Data ............................................................................. 127
6.5 Running the batch processor ................................................... 128
7 XML Toolbox
7.1 Overview .................................................................................. 129
7.2 Opening the XML Toolbox module ............................................ 129
7.3 Extracting data ......................................................................... 130
7.4 Tag definitions ......................................................................... 132
7.4.1
BVA parameters................................................................. 132
7.4.2
DIA parameters.................................................................. 135
8 LWS Integration
8.1 Overview .................................................................................. 139
8.2 Ettan LWS with samples generated within Ettan DIGE system ... 140
8.3 Enter a protein tube generated within Ettan DIGE system,
into Ettan LWS ......................................................................... 142
8.4 Enter a gel, prepared for spot picking within Ettan DIGE system,
into Ettan LWS ......................................................................... 144
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8.5 XML toolbox - LWS Pick List .................................................... 146
8.5.1
Preparations before XML conversion .................................. 146
8.5.2
Using the LWS Pick List tool .............................................. 147
8.5.3
After XML conversion......................................................... 148
8.6 XML Toolbox - Protein Identification ......................................... 148
8.6.1
Preparations in Ettan LWS before XML conversion.............. 149
8.6.2
Using the Protein Identification tool.................................... 150
8.7 Importing protein data templates into the BVA module ............. 151
8.8 Database queries in DeCyder BVA ........................................... 151
9 Tutorials Introduction
9.1 DeCyder Differential Analysis Software structure ...................... 155
9.2 Scope of tutorials ..................................................................... 157
10 Tutorial I - Using DIA module for preliminary
investigation of protein changes
10.1 Objective ................................................................................. 159
10.2 Overview ................................................................................. 159
10.3 Experimental design ................................................................ 160
10.4 Analysis of control-control gel ................................................. 160
10.4.1 Selecting gel images .......................................................... 160
10.4.2 Spot detection ................................................................... 162
10.4.3 Assigning an area of interest .............................................. 165
10.4.4 Gel artifact removal............................................................ 166
10.4.5 Ascertaining the 2 S.D. (standard deviation)
threshold value.................................................................. 168
10.5 Analysis of control-treated gel .................................................. 168
10.5.1 Selecting gel images .......................................................... 168
10.5.2 Spot detection ................................................................... 168
10.5.3 Spot filtering ...................................................................... 168
10.5.4 Setting a threshold............................................................. 169
10.6 Assigning protein of interest ..................................................... 170
10.6.1 Selecting spots for picking ................................................. 170
10.6.2 Spot confirmation .............................................................. 171
10.6.3 Exporting the Pick List and physically excising spots
from the gel....................................................................... 172
11 Tutorial II - Employing an internal standard to
Analyze Protein Changes
11.1 Objectives ............................................................................... 173
11.2 Overview ................................................................................. 173
11.3 Experimental design ................................................................ 174
11.4 Spot detection and quantitation ............................................... 175
11.4.1
Selecting gel images ......................................................... 175
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11.4.2 Spot detection and quantitation.......................................... 176
11.4.3 Viewing the DIA workspace ................................................ 177
11.4.4 Exporting spot map data .................................................... 179
11.5 Creating the BVA workspace .................................................... 180
11.5.1 Selecting gels..................................................................... 180
11.5.2 Function assignment.......................................................... 182
11.5.3 Experimental group assignment ......................................... 183
11.5.4 Landmarking ..................................................................... 184
11.6 Assessing matching ................................................................ 186
11.7 Post-matching landmarking ..................................................... 189
11.8 Statistical analysis .................................................................... 190
11.9 Assigning spots as proteins of interest ...................................... 193
11.9.1 Selecting proteins of interest .............................................. 193
11.9.2 Spot confirmation in Protein Table...................................... 195
11.9.3 Exporting the Pick List and physically excising spots
from the gel ....................................................................... 195
12 Tutorial III - Processing the Preparative Gel and
Generating a Pick List
12.1 Objective ................................................................................. 197
12.2 Overview .................................................................................. 198
12.3 Experimental design ................................................................ 198
12.3.1 Identifying protein spots on SYPRO Ruby stained gel.......... 199
12.3.2 Spot detection.................................................................... 200
12.3.3 Assigning an area of interest .............................................. 203
12.3.4 Gel artifact removal ............................................................ 206
12.4 Matching to analytical gels ....................................................... 208
12.4.1 Preparing the workspace.................................................... 208
12.4.2 Editing picking reference markers ...................................... 211
12.4.3 Matching and landmarking ................................................ 212
12.4.4 Exporting the Pick List........................................................ 216
13 Tutorial IV - Fully automated identification of
differentially expressed proteins
13.1 Objective ................................................................................. 217
13.2 Overview .................................................................................. 217
13.3 Experimental design ................................................................ 218
13.4 Protein spot filtering (optional) ................................................. 219
13.4.1 Selecting gel images .......................................................... 220
13.4.2 Spot detection.................................................................... 221
13.4.3 Gel artifact removal ............................................................ 224
13.5 Processing multiple images ...................................................... 226
13.5.1 Setting up the batch processor........................................... 226
13.5.2 Assignment of spot map attributes ..................................... 229
13.5.3 Protein statistics................................................................. 231
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13.5.4
13.5.5
13.5.6
13.5.7
Protein Filter...................................................................... 232
Saving the Pick List ........................................................... 233
Saving the batch workspace .............................................. 234
Running the batch processor............................................. 235
Appendices
Appendix A:
Appendix B:
Appendix C:
Appendix D:
Appendix E:
Recommended workflow for analysis of multiple gels...
Understanding the digital image..................................
Spot processing algorithms .........................................
Experimental design and set up examples ...................
DeCyder Differential Analysis Software
keyboard shortcuts
Appendix F: Related products and documentation..........................
Appendix G: Glossary......................................................................
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243
251
257
267
271
275
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Introduction to DeCyder Differential Analysis Software
1 Introduction to DeCyder Differential Analysis
Software
1.1 Introduction
Two-dimensional electrophoresis (2-D electrophoresis) is a leading tool
in proteomics research today, capable of visualizing many components
of complex proteomes in a single gel. Ettan™ DIGE (Difference Gel
Electrophoresis) system is a method for pre-labelling protein samples
prior to 2-D electrophoresis. The system is based upon the specific
properties of CyDye™ DIGE Fluor dyes which enable multiplexing of
separate protein mixtures on the same 2-D gel.
DeCyder™ Differential Analysis Software is an automated image
analysis software suite which enables detection, quantitation, matching
and analysis of Ettan DIGE system gels. The software was developed as
part of Ettan DIGE system, to exploit the multiplexing capabilities of
the CyDye DIGE Fluor dyes. Multiplexing, the co-migration of more
than one sample per gel, enables the inclusion of an internal standard.
The internal standard is used to derive statistical data within and
between gels. This experimental design using the internal standard,
effectively eliminates gel-to-gel variation, allowing detection of small
differences in protein levels to be achieved. Using DeCyder Differential
Analysis Software, system variability is minimized enabling expression
differences identified by 2-D DIGE to be confidently assigned to
induced biological change.
1.2 The DeCyder Differential Analysis Software User Manual
This user manual is broadly divided into two main parts, the reference
manual (Chapters 1-8) and the tutorials (Chapters 9-13).
It is recommended that new users first work through the tutorials, in
order to gain a rapid understanding of the software’s capabilities. The
tutorials are step by step guides that take the user through the main
applications of the software by employing real data. A CD containing
the necessary files is required to perform each of the tutorials. The
tutorials are designed to be worked through without prior knowledge
of the reference component of the manual.
The reference manual provides a detailed technical account
encompassing all aspects of the built-in functionality of DeCyder
Differential Analysis Software, which can be used as a source of further
information for experienced users.
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1.3 Measuring differential protein abundance using
Ettan DIGE system
To compare protein abundance in different samples, conventional 2-D
methods require the separation of each sample on an individual gel.
This “one-sample-per-gel” approach exposes the data to a high level of
system variation, i.e. the variation that arises from differences in
protein uptake into the first dimension strip, second dimension gel
running, etc. This high level of system variation can outweigh the often
subtle, induced biological changes that the experiment is intended to
detect, for example, differences that are caused by a disease state, drug
treatment or life-cycle stage.
To compound this problem, it is also necessary to separate the induced
biological changes within an experiment from the inherent biological
variation, i.e. the differences between two individual animals, cultures,
plants or flies, that are present irrespective of the applied experimental
test conditions. To achieve this, multiple sample replicates must be
incorporated within each experimental design. This requires the
separation and analysis of a large number of samples and can be a slow
process if each sample is separated on a different gel.
Ettan DIGE system has been developed to address these problems. The
system includes CyDye DIGE Fluor Cy™2, Cy3 and Cy5 minimal dyes,
which are mass and charge-matched, spectrally resolvable fluorescent
dyes. Additional CyDye DIGE Fluor Cy3 and Cy5 saturation dyes have
been developed specifically to be used where only small amounts of
sample are available. Please refer to section F.1 for ordering details of
the Scarce Sample Labelling kit. A protein sample labelled with any of
the CyDye DIGE Fluor dyes, will migrate to the same position on a
2-D gel. This permits the multiplexing of two or three samples within
the same 2-D gel, allowing the inclusion of an internal standard (see
section 1.4).
Gels are scanned using the Typhoon™ Variable Mode Imager,
generating overlaid, multi-channel images for each gel. Images can then
be analyzed using the DeCyder Differential Analysis Software, which
contains novel algorithms for co-detection of multiplexed gel images
and has been specifically developed for use with Ettan DIGE system
(see section 1.4).
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Fig 1-1. Scheme showing the workflow for Ettan DIGE system
The benefits offered by Ettan DIGE system are:
• Accurate quantitation and statistical analysis of protein abundance
changes
• High sensitivity and wide dynamic range (5 orders of magnitude)
• Minimization of system (gel-to-gel) variation
• Easier matching between gels, with increased confidence
• Fewer gels required per experiment
• Faster analysis due to fully automated gel-processing workflow
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1.4 Experimental Design using an Internal Standard
Ettan DIGE system provides the ability to multiplex samples, enabling
the use of an internal standard within each 2-D gel. Ideally, the internal
standard should consist of a pool taken from all of the samples within
the experiment. The internal standard is labelled with one of the CyDye
DIGE Fluor minimal dyes (usually Cy2 if using CyDye DIGE Fluor
minimal dyes or Cy3 if using CyDye DIGE Fluor saturation dyes) and
run on each gel in the experiment. This creates an image that is the
average of all experimental samples, with all proteins in the experiment
represented. The presence of the internal standard in every gel provides
an intrinsic link between samples. Ettan DIGE system is currently the
only 2-D gel electrophoresis protein difference analysis technique to
utilize the internal standard approach.
There are several benefits of using an internal standard in 2-D
experiments. Firstly, each protein spot in a sample can be compared to
its representative within the internal standard on the same gel, to
generate a ratio of relative protein levels. Quantitative comparisons of
samples between gels are made based on the relative change of sample
to its in-gel internal standard. This process effectively removes the
system (gel-to-gel) variation enabling accurate quantitation of induced
biological change between samples (Fig. 1-2). The need to run gel
replicates is also overcome, reducing the number of gels required per
experiment.
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Fig 1-2. Analysis of samples 1-4 in the absence of an internal standard would
suggest that the protein spot circled in red on gel 1 is absent in samples 3 and 4.
However, reference to the internal standard, which is an identical pool of all
samples run on both gels, shows that this protein has not entered gel 2. This
indicates that the observed absence of this protein spot in samples 3 and 4 is due
to system variation (e.g. gel distortions, differences in first dimension focusing
etc.) and not to sample differences. Similarly, analysis without the internal
standard of the protein spot circled in blue suggests that this protein is present in
a greater abundance in sample 4. Reference to the internal standard indicates
that system variation has resulted in this protein having an increased volume in
gel 2 relative to gel 1. Hence the abundance of this protein is unchanged in
samples 1,2 and 4 and decreased in sample 3.
A further benefit of using an internal standard is that matching between
gels is more straightforward. The internal standard image is common
between all gels in an experiment, therefore matching can be performed
between internal standard images which have the similar spot patterns.
Conventional 2-D electrophoresis requires matching between different
samples on different gels, which introduces differences in spot patterns
from sample-to-sample and gel-to-gel variation. Matching between
internal standards allows matching between identical samples, so
variations in spot patterns are due only to electrophoretic differences.
The internal standard approach can be applied to two-color or threecolor experiments by including the internal standard plus one sample
or two samples respectively on each gel.
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To maximize the quality of the data obtained when working with Ettan
DIGE system, the correct experimental design should be implemented.
Table 1-1 shows an example of the experimental design used for a
simple three-color experiment using control and treated groups, each
containing four individuals
Gel
1
2
3
4
Pooled
Pooled
Pooled
Pooled
Cy2
internal standard
internal standard
internal standard
internal standard
Cy3
Control A
Control B
Treated D
Treated A
Cy5
Treated B
Treated C
Control C
Control D
Table 1-1. Using an internal standard for accurate measurement of protein abundance changes between samples in an experiment (for an experiment using
>2 samples).
Each gel contains a pooled internal standard labelled with CyDye DIGE
Fluor Cy2 minimal dye. Four biological replicates (A-D) have been
included for control and treated samples that have each been labelled
with CyDye DIGE Fluor Cy3 or Cy5 minimal dyes. A greater degree of
statistical confidence can be assigned to the experimental results by
increasing the number of biological replicates employed. Half of the
control group are labelled using the CyDye DIGE Fluor Cy3 minimal
dye, half using the CyDye DIGE Fluor Cy5 minimal dye and similarly
for the labelling of the treated group, thereby confirming to best
experimental practices.
For more detailed information about Ettan DIGE system and using an
internal standard, please refer to Ettan DIGE system User Manual
(code no. 18-1173-17).
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1.5 Integration of DeCyder Differential Analysis Software
with Ettan DIGE system experimental design
DeCyder Differential Analysis Software was developed specifically for
the 2-D DIGE methodology and therefore all the advantages of this
approach are utilized in the software.
• DeCyder Differential Analysis Software in conjunction with Ettan
DIGE system allows the analysis of experimental designs with
various degrees of complexity. A simple control – treated
experiment through to a multi-condition experiment addressing
factors such as dose and time can be performed in a single analysis.
• The relationship between any number of samples can be accurately
quantified and statistically analyzed in DeCyder Differential
Analysis Software by employing the internal standard (see
Fig. 1-2). This approach results in unparalleled accuracy allowing
experimental conclusions to be drawn with high confidence. No
other 2-D electrophoresis technique available is capable of
resolving multiple samples in this manner and hence Ettan DIGE
system is unique in exploiting use of an internal standard on every
gel.
• The novel co-detection algorithm exploits the identical spot
patterns generated when multiple samples are resolved on the same
gel. The algorithm generates identical spot detection patterns on all
images derived from the same gel. Hence all spots on the same gel
are effectively matched with the identical spot boundaries.
• Spot quantitation is performed automatically by normalizing spot
volumes against the internal standard (Fig. 1-3). The co-detection
algorithm ensures that the internal standard and the quantified
analytical spot have an identical spot boundary. This results in a
highly accurate and robust protein quantitation.
• DeCyder Differential Analysis Software utilizes experimental
design incorporating an internal standard and performs gel to gel
matching on the standard samples.
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Fig 1-3. A. DeCyder Differential Analysis Software utilizes an internal standard to
a) aid spot matching between samples within the same gel and b) generate a ratio
of protein abundance between the proteins of the internal standard and each
sample within the same gel. Because the internal standard is the same sample
run within each gel, this effectively normalises all the data. These functions are
performed within the Differential In-gel analysis (DIA) module of the software.
B. The Biological Variation (BVA) module is used to provide a quantitative comparison of protein abundance between all samples within the experiment.
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1.6 Steps involved in Image Analysis using
DeCyder Differential Analysis Software
Image analysis performed using DeCyder Differential Analysis software
is performed using a number of complex algorithms, some of which are
patent pending. They have been designed specifically for use with
multiplexed 2–D images. These steps can be broken into the following
processes:
• Spot detection
• Background subtraction
• In–gel normalization
• Gel artifact removal
• Gel to gel matching
• Statistical analysis
The complex algorithms associated with these steps form part of the inbuilt functionality of the DeCyder Differential Analysis Software. The
various stages of gel processing are performed by different modules
within the software suite.
1.7 Structure of DeCyder Differential Analysis Software
The software consists of four modules (see Fig. 1-4):
DIA (Differential In-gel Analysis) – Protein spot detection and
quantitation on a set of images, from the same gel.
BVA (Biological Variation Analysis) – Matches multiple images from
different gels to provide statistical data on differential protein
expression levels between multiple groups.
Batch Processor – Fully automated image detection and matching of
multiple gels without user interaction.
XML Toolbox – Extracts user specific data facilitating automatic report
generation.
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Fig 1-4. Structure of DeCyder Differential Analysis Software
DIA module
A set of images (each saved as either 16-bit TIFF or customized .GEL
file) from a single gel are loaded then processed simultaneously. The
DIA algorithms perform spot detection on a combined image derived
from all loaded images. The protein spot quantitation is calculated and
expressed as a volume ratio to the internal standard. The results can be
saved as a .DIA file, which can be re-opened in the DIA module. This
pick list is created from data generated in a single DIA module analysis
(i.e. an experiment based on a single gel). Alternatively, the generated
results can be saved in an XML format, or opened in the XML Toolbox
module in order to extract specific data, or exported to the BVA module
for multi-gel analyses (Fig. 1-5).
Fig 1-5. Schematic representation of DIA module workflow
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BVA module
The BVA module utilizes the XML files exported from the DIA module
together with the original gel images to match protein spots on different
gels. Data is then subjected to statistical analyses to accurately assess
protein changes occurring between control and test groups within the
experiment. The results can be saved as .BVA file, which can be reopened in the BVA module. Furthermore, data can be exported in XML
format for data extraction using the XML Toolbox.
Fig 1-6. Schematic representation of BVA module workflow
Batch Processor
The Batch Processor integrates both the DIA and BVA modules
enabling fully automated processing of multiple gels without user
intervention. The Batch Processor can be configured to analyze several
gels in the DIA module exclusively. Alternatively multiple gels can be
processed through both modules to produce a final .BVA file and
subsequent pick list.
XML Toolbox
The XML Toolbox enables the extraction of user specific data from the
XML files generated in either the DIA or BVA modules. This data can
be saved in either text or html format enabling users to access data in
DeCyder Differential Analysis Software workspaces for other
applications.
The XML Toolbox also provides an interface for linking Ettan DIGE
system data with Ettan Laboratory Workflow System.
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Introduction to DeCyder Differential Analysis Software
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Computer requirements and installation
2 Computer requirements and installation
2.1 Computer requirements
• Operating System: Windows™ XP.
• Minimum Processor: Pentium™ 4 processor, 1.5 GHz.
• 1 Gbyte RAM.
• Video card capable of 32 bit color.
• The video card driver needs to support Open GL™ (v 1.2 or later) ensure the latest compatible driver is installed.
• The color resolution of the PC should be set to 32 bit color.
• The screen resolution should be set to 1024 x 768 pixels, landscape
(with 24 bits Z-buffer / preferably 32 bits).
• The virtual memory should be set so that the total amount of
available memory, including physical RAM, is greater than 2.5 GB.
• Internet Explorer™ version 5.5 or higher must be installed to run
the XML Toolbox module.
Note: Avoid running other programs at the same time as the various
DeCyder Differential Analysis Software modules.
2.2 Installation
DeCyder Differential Analysis Software is protected by a HASP™ key
that should be attached to either the USB or parallel port on the
computer when DeCyder Differential Analysis Software is in use. The
HASP device driver software is installed during installation of DeCyder
Differential Analysis Software. The color resolution must be set to at
least 32 bit color and the screen resolution should be set to
1024 x 768 pixels. These settings are accessed on the Settings tab in the
Display icon on the Control Panel. Local administrative privileges must
first be obtained for installation of DeCyder Differential Analysis
Software.
Insert the DeCyder Differential Analysis Software CD-ROM and select
the appropriate disc drive in Windows explorer. From the files on the
disc double click the Setup icon.
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2.2.1 Installation with a previous version present
If a previous version of DeCyder Differential Analysis Software was
installed select Reinstall all DeCyder components installed by the previous
setup from the dialog box to remove the old version and automatically
install the new version. Click Next.
Note: If DeCyder Differential Analysis Software version 4 or earlier is
already installed, the installation process will continue as a
de novo installation (see section 2.2.2) automatically removing
the previous version of the software.
DeCyder Differential Analysis Software can be un-installed
automatically by selecting Remove all installed DeCyder components.
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2.2.2 De Novo Installation
If DeCyder Differential Analysis Software has not been previously
installed on the system a different dialog box will appear when the
Setup icon is selected. Click Next, to install DeCyder Differential
Analysis Software.
In the subsequent dialog box, a default file path for the installation is
given, if this is not suitable browse for an alternative path. Click Next,
to install the software to the selected path.
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The HASP key and DeCyder Differential Analysis Software will now be
installed. When setup is complete click Finish.
Note: If DeCyder Differential Analysis Software is supplied preinstalled on a computer, no further software should be installed.
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3 DIA (Differential In-gel Analysis) Module
3.1 Overview
DIA Processes
DeCyder Differential Analysis Software DIA processes images from a
single gel, performing spot detection and quantitation.
The DIA module algorithms detect spots on a cumulative image derived
from merging up to three individual images from an in-gel linked image
set. This co-detection ensures that all spots are represented in all images
processed. The DIA module algorithms then quantitate spot protein
abundance for each image and express these values as a ratio thereby
indicating changes in expression levels by direct comparison of
corresponding spots. This ratio parameter can be used, in small scale
experiments, to directly evaluate changes between two labelled protein
samples. Alternatively, the ratio can be used for protein spot
quantitation of a sample against an internal standard to allow accurate
inter-gel protein spot comparisons (see Chapter 1). Once spot maps
incorporating an internal standard have been analyzed in the DIA
module, the spot data can be used in DeCyder Differential Analysis
Software BVA for accurate quantitative inter-gel studies. Generally
when multiple gel analyses are performed, only the spot detection and
quantitation algorithms are utilized in the DIA module. Data is then
transferred to the BVA for inter-gel analysis.
Data can be saved and re-opened in a .DIA file format from within the
DIA module. The DIA module can also export for both Ettan Spot
Picker or Ettan Spot Handling Workstation. Data can also be exported
in an XML format for either multi-gel analysis in DeCyder Differential
Analysis Software BVA, querying in DeCyder Differential Analysis
Software XML toolbox or copying and pasting into applications such
as Microsoft™ WORD™ and EXCEL™.
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DIA Graphical User Interface
DeCyder Differential Analysis Software DIA graphical user interface is
divided into four equally sized inter-linked views.
• Image View - primary and secondary gel images
• 3-D View - a three dimensional representation of the gel localized
on the spot
• Histogram View - graphical representation of data associated with
the spots displayed in the image view
• Table View - tabulated data associated with selected spots
displayed in the image view
All four views are linked, therefore selecting a spot in, for example the
Image View, will display the spot in the Histogram View (in magenta),
the 3-D View and the Table View. The role of the four views will be
discussed in detail later in this section.
Below the four views the Data Control Panel is found. This panel
contains tools and user definable functions associated with the specific
data displayed in each mode.
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3.2 Creating and opening workspaces
Creating Workspaces
From the Desktop or Start button menu select the DeCyder Differential
Analysis Software - DIA icon to open the software module. Create a new
workspace by selecting File:Create Workspace in order to load the images
for analyzing.
There are three distinct algorithms employed for image spot detection,
which process different numbers of images simultaneously.
• Single detection - one image
• Double detection - two images
• Triple detection - three images
Select the detection algorithm appropriate for the number of images
present on the gel being analyzed, then click OK.
In the resulting window click on the image file that is to become the
primary image, and click Open.
Note: DeCyder Differential Analysis Software has been validated for
file formats generated by Amersham Biosciences imaging devices
recommended for Ettan DIGE system (see Appendix B).
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Repeat for the secondary and tertiary images if required.
Note: In an experiment in which an internal standard sample is being
used, the standard sample image is designated as the primary
image and the analytical samples are designated as the secondary
and tertiary images.
Once the image(s) have been loaded spot detection and quantitation
can be performed.
Opening Workspaces
To open previously created and saved workspaces select File:Open and
browse to locate the DIA file. When the file is located select the file and
click Open or double click on the DIA file.
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Workspace Properties
Properties associated with the workspace are defined in the Workspace
Properties window. Selecting View:Properties… (or selecting the Properties
icon) then selecting the Workspace tab, displays the workspace
properties window.
The dye tag used and the dye chemistry used can be selected from the
pull down menus. The dye tag label is automatically selected if the
information is included in the image file name. The remaining
information is derived from the data processing performed.
3.3 Spot detection and quantitation
3.3.1 Detection
There are three distinct spot detection algorithms, which are able to
simultaneously process a different number of images derived from a
single gel.
• Single detection - one image
• Double detection - two images
• Triple detection - three images
Single detection is performed on images of post-stained gels used for
picking, a case where there is a single image associated with the gel.
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Double and triple detection algorithms are designed to take advantage
of the inherent co-migration benefits of the CyDye DIGE Fluor dyes. A
set of co-run images (two images in double detection and three images
in triple detection) is merged together thereby incorporating all spot
features in a single image. Spot detection and spot boundary definition
is then performed using pixel data from all the individual raw images
and the merged image. The resultant spot map is overlaid back onto the
original image files. Since the spot boundaries are identical for all
images, the spots are effectively already matched. This process results
in highly accurate volume ratio calculations.
When creating workspaces the user selects the applicable algorithm and
is subsequently prompted to load the appropriate number of image
files. Once the images are loaded the pre-selected spot detection
algorithm can be applied.
Select Process:Process Gel Images to open the Process Gel Images
window.
An estimation of the number of spots present on the images must be
entered, before clicking OK. For example, a mammalian lysate run on
an 18 cm pH 4-7 Immobiline™ DryStrip and a large format gel, such
as the Ettan DALT electrophoresis unit (20 cm x 26 cm), a value of
2500 for Estimated Number of Spots should be satisfactory. If all the
spots have not been identified the spot detection process can be
repeated with a higher number of estimated spots. It is recommended
that this value be overestimated to compensate for the detection of nonproteinaceous spots on the image, e.g., dust particles which are
subsequently excluded from the analysis.
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3.3.2 Quantitation
Numerical data for individual spots are calculated (e.g. volume, area,
peak height and slope).
Spot volumes (sum of pixel intensities within the spot boundary) are
always expressed with background subtracted. Background is
th
subtracted on a spot specific basis, by excluding the lowest 10
percentile pixel value on the spot boundary, from all other pixel values
within the spot boundary. The spot volume is the summation of these
corrected values.
Spot ratios are calculated (volume of secondary image spot/volume of
primary image spot). This ratio indicates the change in spot volume
between the two images.
These ratio values are normalized, so that the modal peak of volume
ratios is zero (since the majority of proteins are not up or down
regulated). This ratio parameter is referred to as the volume ratio. In all
DeCyder Differential Analysis Software DIA tables the volume ratio is
expressed in the range of 1 to ∞ for increases in spot volumes and
–1 to – ∞ for decreases in spot volumes. Values between –1 and 1 are
not represented, hence a two-fold increase and decrease is represented
by 2 and –2, respectively (not 2 and 0.5).
When using either double or triple detection algorithms, the images
displayed in the primary and secondary views can be selected using the
drop down in the Image View title bar. Spot quantitation is
automatically recalculated upon alteration of the primary and
secondary image. When using single detection the volume ratio value is
left blank, since there is no secondary image.
Note: The volume ratio is displayed in the Table View in this manner
but all data analyses are based on the log of the normalized ratio
measurement (see Appendix C for further details).
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3.4 Viewing spot data
3.4.1 Image View
Image View simultaneously displays both primary and secondary gel
images.
Five toolbar functions are associated with the Image View. All these
functions can also be accessed through menu pull down options,
indicated in parentheses.
(View:Image View). Expands the Image View to fit the workspace.
(View:Zoom:Zoom In). Zooms in to selected region of the image.
(View:Zoom:Zoom Out). Zooms out of selected region of the image.
(View:Zoom:Fit to window). Fits the entire gel image to Image View
window.
(View:Contrast Brightness). Changes the brightness and contrast of the
Image View. Raising the positions of the slide bar increases either the
contrast or brightness. Selecting the Apply to all Images check box results
in linking the controls to all images. Altering the contrast and
brightness does not change the raw pixel data contained within the
image, hence subsequent analyses are not effected
Zooming in and out of the image can also be performed using the
mouse by dragging the mouse over a square area of the image to be
zoomed. Dragging the mouse from top left to bottom right results in
zooming into the image, whilst dragging the mouse from bottom right
to top left result in resizing the image to fit.
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Properties
Properties associated with Image View are defined in the Image View
properties window. Selecting View:Properties… or selecting the Properties
icon then selecting the Image View tab displays the Image View
properties window.
Changing the default radius of picking references allows the user to
define the reference marker for Ettan Spot Picker.
The default picking head diameter is used to define the size of the
automated picking head. For example, Ettan Spot Picker manual
recommends a 2 mm picking head diameter. Therefore with a
100 micron resolution image this translates to a 20 pixel picking head
diameter (the default value).
Selecting the Auto-center selected spots check box results the image view
automatically shifting so that the selected spot is in the center of the
view.
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3.4.2 3-D View
The 3-D View function provides a three dimensional representation of
the primary and secondary images localized on the selected spot,
representing the raw image without filtering. The representation is
plotted along the X-Y axes in the plane of the gel. The Z axis scale is
normalized between the two images based on the volume ratios,
facilitating direct visual comparison between the two
3-D spot images.
Three toolbar functions are associated with the 3-D View. All these
functions can also be accessed through menu pull down options.
(View:3-D View). Expands the 3-D View to fit the workspace.
(View:Area in 3-D). Displays the area selected in the Image View in the
3-D View.
(View:Rotate 3-D). Rotates the 3-D View, re-clicking the icon stops
rotation.
The 3-D View can be rotated manually by dragging the mouse over the
3-D image.
Adjustment of the 3-D View can be performed using the mouse.
Holding down both mouse buttons and dragging the mouse upwards
and downwards, results in zooming in and out the 3-D View,
respectively.
The position of the spot image within the 3-D View can be adjusted by
holding down the right mouse button then dragging the mouse over the
3-D View.
Data associated with each of the highlighted spots in the 3-D View are
displayed beneath the corresponding spot.
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The orientation of the scaling of the 3-D View can be reset to the initial
viewing settings by selecting the Reset button above in the 3-D View.
Spot number
Position
Volume
Peak Height
Area
Pick Position
Spot reference number (unique to a codetected spot on a set of images from the same
gel).
Gel X,Y co-ordinates of the co-detected spot
Spot pixel volume
Largest pixel value within the spot boundary
(expressed with background subtraction).
Number of pixels within the spot boundary
Gel X,Y co-ordinates of the spot pick location
Properties
Properties associated with 3-D View are defined in the 3-D View
properties window
Selecting View:Properties… or selecting the Properties icon then selecting
the 3-D View tab, displays the 3-D View properties window.
The size of area displayed in the 3-D View can be altered by entering a
positive integer between 3 and 80.
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3.4.3 Table View
Displays data associated with selected co-detected spots, in a tabulated
format. The data within the table can be sorted into ascending or
descending order by clicking on the column headers of the table.
Clicking on the Table View icon (or selecting View:Table View) expands the
Table View to fit the workspace.
The following information on co-detected spots is contained within the
Table View. The spots displayed in the table can be adjusted using the
Table View Properties Tab (see section 3.5.1).
Status
Indicates whether a spot has been confirmed by
the user (see section 3.5.1).
Spot No.
Spot reference number (unique to a spot pair on
a set of images).
Abundance
“Decreased”, “Similar” or “Increased”
depending on thresholds set in DIA Histogram
View
Excluded
Spot assigned by user or Exclude Filter for
removal from analysis set. An excluded spot is
never completely removed from the workspace
and can be recovered by the user. Excluded spots
are not imported to BVA.
Volume Ratio
Normalized Volume Ratio between co-detected
spots in the primary and secondary images.
Picked
“Pick” designation indicates that a spot has been
selected for picking (see section 5.4.1)
Max Slope
Largest gradient associated with co-detected
spots.
Area
Number of pixels within the spot boundary
Max Peak Height Largest pixel value associated with co-detected
spots.
Max Volume
Volume of the largest of the co-detected spots.
Protein ID
User defined protein identification (manually
entered in the Protein ID text box at the bottom
of the screen).
Comment
User defined comment (manually entered in the
Comment text box at the bottom of the screen).
Function
Indicates whether a spot has been selected as a
protein of interest denoted by the letter “I” in the
column
PTM
Indicates whether a spot has been selected as a
protein that carries a post translational
modification denoted by the letters “PTM” in the
column
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The following diagram illustrates some of the parameters in the Table
View associated with a spot.
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3.4.4 Histogram View
The histogram view is only visible when more than one image is being
analyzed (i.e. analyses employing either double or triple detection
algorithms).
Clicking on the Histogram View icon (or selecting View:Histogram View)
expands the Histogram View to fit the workspace.
Histogram
The histogram displays data associated with all detected spots in the
primary and secondary images. Spot data is plotted against log volume
ratio on the X-axis, using two Y-axes.
• Left Y-axis: displays the spot frequency. The red curve represents
the frequency distribution of the log volume ratios. The curve in
blue represents a normalized model frequency fitted to the spot
ratios so that the modal peak is zero (see Appendix C).
• Right Y-axis: represent the scatter parameter selected in the
histogram selection box (right of the histogram). A plotted single
data point on the histogram represents an individual protein spot.
The histogram is automatically recalculated when the primary and
secondary images are changed using the pull-down menu in the Image
View title bar.
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Histogram Selection
The Histogram Selections toolbar contains two drop down menus and
two information boxes: Scatter Parameter and Threshold mode.
Scatter Parameter
The pull down menu can be used to plot either maximum peak height,
area, maximum slope, maximum volume (displayed on the right Y- axis
of the histogram) against log volume ratios.
Threshold mode
Thresholds are user-defined values that enable highlighting of spots
that differ between the primary and secondary image. Consequently,
the threshold functionality is predominantly used when performing
small-scale experiments to determine expression differences in two
samples run on a single gel.
A variety of values can be selected in the threshold pull-down menu.
• Model S.D. (standard deviation): number of standard deviations
based on the normalized model curve displayed in the histogram
view.
• Fold: magnitude of volume ratios.
• Manual: user-defined magnitude of volume ratios (entered in the
threshold text box).
The threshold values are represented by vertical black lines in the
histogram. Spot boundaries in the Image View and data points in the
histogram are automatically color coded to denote the spots that are
below, within or above the threshold values. The abundance column in
Table View is also updated automatically.
Threshold
Indicates the volume ratio selected in the threshold mode pull down
menu (e.g. the volume ratio represented by 2 model S.D.).
2 S.D.
Indicates the volume ratio for 2 S.D. based on the raw data. In a
normally distributed data set 95% of data points fall within this value.
Spot statistics
Displays information on the spot population illustrated in the
histogram view based on the user-defined threshold settings.
• Decreased: the number of spots classified as decreased in their
abundance in the primary image, compared to the secondary
image.
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• Similar: the number of spots classified as not differentially
expressed in the primary image, compared to the secondary image.
• Increased: the number of spots classified as increased in their
abundance in the primary image, compared to the secondary
image.
The Table View, Image View and Histogram View can be adjusted to
selectively display any of these subgroups by selecting the Spot Display
and Table View tabs in the properties dialog box then
selecting/de-selecting the appropriate check boxes.
The number of spots designated as excluded and included (designated
either manually or by the Exclude Filter) are displayed, see next section
for more details.
Workspace information
Displays frequency data on the various categories of spots contained
within the entire workspace.
3.5 Data analysis
The DIA module does not require the user to perform any form of spot
boundary editing. After spot detection the user only has two post
detection tasks to perform.
1
Creation and execution of an exclusion filter to remove nonproteinaceous spots.
2
Confirmation of detected spots. This is used for small-scale
experiments to confirm expression differences in two samples run
on a single gel.
This approach was employed to remove user-to-user variation from
software analysis. In-house studies have shown that different users
generate statistically different results if spot editing is permitted.
The spot detection process (DIA) in DeCyder Differential Analysis
Software has been demonstrated to be highly accurate (in-house studies
have demonstrated 98% accuracy). The accuracy achieved without
editing is taken as an acceptable level and is more than outweighed by
the removal of user-to-user variation and higher throughput from not
editing.
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3.5.1
Spot exclusion
Assigning an area of interest
The automated spot detection algorithm in DeCyder Differential
Analysis Software DIA may detect artifacts due to gel heterogeneity at
the edges of the images. These spots can be removed by setting an area
of interest to define the gel only. Those spots outside of the area of
interest will be automatically removed when the Exclude Filter is
applied. The area of interest function is only operational when the
Exclude Filter is executed. If an area of interest was set before detection
all the spots on the image will still be detected (even those outside the
area of interest).
To set an area of interest click on the Fit to window icon on the tool bar
to fit the gel images to the Image View.
Click on the Image View icon on the tool bar to have a full screen view
of the gel images.
Click on the Properties icon and select the Spot Display tab in the window
that now appears. De-select Similar, Increased and Decreased and Click
OK, to remove the spot boundaries displayed in the Image View (deselecting the spot boundaries on view is optional but helps the user to
see the gel more clearly).
Select Edit:Define Area of Interest and using the rectangular target pointer
drag the mouse to draw a rectangle on either of the gels, taking care to
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exclude edge artifacts. The area of interest is automatically defined on
the other image(s). Click on the Properties icon and select the Spot
Display tab and reselect the Increased, Decreased and Similar options and
click OK.
Note: To remove the area of interest select Edit:Remove area of
interest.
Creating an Exclude Filter
The Exclude Filter removes specific spots (based on their physical
characteristics) in order to eliminate non-proteinaceous spots from
further analyses.
Select Process:Exclude Filter (or select the icon from the toolbar) to open
the Exclude Filter dialog box.
The 3-D View generally reveals if a detected spot is a gel artifact rather
than a protein spot. Dust particles normally have very steep sides and a
pointed peak when compared to the smoother curve of a protein spot.
Therefore dust particles have high slope values and low area values.
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The figure below illustrates a protein spot and a dust particle spot.
Irregularities in the gel surface may also be detected as a spot, however
such artifacts have low peak height and volume values.
Artifact spots can therefore be excluded on the basis of slope, area,
peak height and volume.
The appropriate filter parameter values can be efficiently obtained by
ordering the table according to each parameter in the filter. To order the
table according to Max Slope click once on the Max slope header on the
table so that the largest maximum slope is at the top of the column (if
the smallest maximum slope is at the top, click again).
Click on the first spot in the Table View and observe the spot in the
3-D View. The 3-D View clearly shows if the detected spot is a dust
particle rather than a protein spot. Working down the table leads to
spots with smaller and smaller Max Slope values. After a certain value
the spots change from gel artifacts to real protein spots. The max slope
value of the artifact immediately before the real protein spot in the table
is the value entered into the slope parameter of the filter. The same
procedure can be applied with the Area, Peak Height and Volume. The
only difference with finding these parameters is that the table has to be
ordered so that the smallest value is present at the top of the table
before scrolling down. If triple detection has been employed, it may be
necessary to toggle between the three images, to ascertain more
accurately the exclude filter parameters.
After the filter parameters have been established select Process:Exclude
Filter to display the Exclude Filter window and enter the values
determined, click OK. The Exclude Filter will automatically remove
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detected spots on the basis of these values. In addition those spots
outside of the area of interest will also be removed.
Note: These excluded spots can be returned to the experiment by deselecting the check boxes in the Exclude Filter window and then
re-running the filter. Alternatively, the values in the exclude filter
can be edited, followed by re-running the filter. Excluding spots
only removes them from the analysis, not from the workspace.
Manual spot exclusion
Non-proteinaceous spots that are not removed by the exclusion filter
can be manually removed from the data set by highlighting the spot
then selecting the Exclude check box at the bottom of the screen.
Select Process:Re-Normalize to perform the normalization process again
with the manually excluded spots removed from the calculation.
Note: Re-normalization is automatically performed when the Exclude
Filter is used.
3.5.2 Spot confirmation
Remaining, non-excluded spots can be manually confirmed, for the
purposes of visually verifying each spot. Three options are available
during spot confirmation:
1
46
Only confirm spots that are increased or decreased in their
abundance. This method is relatively rapid since the increased and
decreased spots are automatically identified by setting the threshold
mode (see section 3.4.4). There is often very little benefit in
investigating spots that do not change in expression levels.
Select View:Properties, and select the Table View tab. Ensure in this
window that the Decreased and Increased options as well as Picked
Spots are ticked, and that the Excluded box is not ticked. Click OK.
It is useful to have the Confirmed option checked.
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2
Only confirm spots that have been assigned with a pick status. This
is the recommended option if the protein filter has been applied
prior to spot confirmation.
Select only Picked spots in the Table View tab. The procedure is then
identical to option 1.
3
All spots (Decreased, Increased and Similar) are manually verified
(this takes approximately 1.5 hours for 1000 spots and is not
recommended).
Select the Similar, Decreased and Increased options on the Table View
tab. Click OK. The procedure is then identical to option 1.
With the above options, a convenient way to start is to sort the Table
View data based on a spot characteristic (e.g. Max Volume). Click once
on the column header entitled Max Volume. Scroll to the top of the table
to see whether the spot with the largest volume has been sorted to the
top of the table (if the spot with the lowest volume is at the top of the
table click once more on the max volume header). The spots with the
largest Max volume (and hence at the top of the sorted table) are almost
exclusively protein spots, which can be quickly confirmed. Scrolling
down the table to spots with lower Max volumes, non-proteinaceous
spots start to appear which can be manually excluded.
Spot confirmation pertains to a Spot Number, therefore when using
triple detection a confirmed spot number will possess the confirm status
irrespective of which spot maps are displayed in the image view.
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3.5.3 Protein of Interest
Protein spots can be assigned as a Protein of Interest, thereby highlighting
these spots for further investigation. Protein of Interest status can be
assigned manually by selecting the Protein of Interest check box when
highlighting the desired protein spot.
Alternatively, the Protein filter can be used to automatically assign
Proteins of Interest using spot property as a selection criteria.
3.5.4 PTM assignment
Protein spots possessing a post-translational modification (PTM)
identified by methods outlined in Ettan DIGE system application notes
(e.g. application note 18-1170-83 AA, obtainable on the Amersham
web site) can be denoted by checking the PTM check box.
3.6 Customizing display colors
The colors used in the various views can be customized by selecting the
Colors tab in the Properties dialog box.
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Spot colors: The colors of spot boundaries displayed in the Image Views
and the spot colors in the histogram view can be altered by clicking on
the colored circles and selecting a new color.
Histogram colors: The colors of the line graphs in the histogram view can
be altered by clicking on the colored circles and selecting a new color.
Clicking Default restores the original settings.
3.7 Saving, exporting and printing
Save As
Workspaces are saved as DIA files (.dia file extension), which contain
all the data associated with all the loaded spot maps.
Select File:Save As, and browse to locate the folder to save the
workspace in. Enter a file name then click Save.
Save
Save workspace updates the saved version of the file to include any
changes that have been made to the workspace.
Printing
Select File:Print to open the Print dialog box.
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Multiple check boxes can be selected for printing the required
workspace views.
DeCyder Differential Analysis Software software supports the
following printers:
• HP LaserJet 4000N
• HP color LaserJet 5M
• HP 2500C
• HP DeskJet 895 Cxi
• HP DeskJet 950C
• HP Laser Jet 4100 DTN
Note: Other manufacturers and models of printers have been
demonstrated to be compatible with DeCyder Differential
Analysis Software.
Using the clipboard
Any aspect of the different views within the workspace can be copied
to the clipboard for pasting into another application.
Click on the part of the workspace chosen for pasting then select
Edit:Copy (the part of the workspace selected is described in this pull
down menu). The clipboard can then be pasted into other applications.
Exporting data
Each of the export functions in DIA is located under the File menu.
1
Export Result Table
Exports all the data in the Table View in a text format that can be
opened in a spreadsheet.
2
Export pick list
Exports the proteins which have been assigned for picking by the user
in the form of a .txt or .xml file that can be recognized by Ettan Spot
Picker or Ettan Spot Handling Workstation.
3
Export Spot Maps
Exports all the data from the DIA workspace in an XML format that
contains all the data generated in the DIA workspace. This can be
opened by either the DeCyder Differential Analysis Software BVA or
the DeCyder Differential Analysis Software XML toolbox.
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4 BVA (Biological Variation Analysis) Module
4.1 Overview
4.1.1 Functionality
DeCyder Differential Analysis Software BVA processes multiple Ettan
DIGE system gel images, performing gel to gel matching of spots,
allowing quantitative comparisons of protein expression across
multiple gels.
BVA processes gel images that have undergone spot detection in DIA.
The BVA module utilizes the XML files generated in DIA (Section 3.7)
together with the original scanned image files. The images are then
matched to a single master image, identifying common protein spots
across the gels. Various experimental designs can be assigned in BVA,
facilitating the statistical analysis tools to highlight proteins that
demonstrate significant protein changes under different experimental
conditions.
Additional in-built functionality allows post-matching activities to be
performed:
• Molecular weight calculation
• Isoelectric point calculation
• Database linkage
• Statistical analyses
• Spot pick list generation
Data can be saved then re-opened in a BVA file format. Pick lists can be
exported as either a text file or XML file for use in Ettan Spot Picker or
Ettan Spot Handling Workstation, respectively. Data can also be
exported in an XML format for querying in DeCyder Differential
Analysis Software XML toolbox or copying and pasting into
applications such as Microsoft™ Word and Excel™.
4.1.2 Graphical user interface
The BVA graphical user interface is similar to DIA. It is divided into
four equally sized inter-linked views. Selecting a spot in, for example,
the Image View will display the same spot in the Graph View, 3-D View
and the Table View.
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• Image View: displays two gel images.
• 3-D View: a three dimensional representation of the images
localized on the spot selected.
• Graph View, graphical representation of data.
• Table View, tabulated data.
There is also a data control panel present on the bottom of the
workspace, which incorporates specific functionality features. The
contents of the control panel and the Table View are dependent on the
mode selected.
4.1.2 Structure
The BVA is composed of four different modes, which display data
manipulated through various tables and associated controls. Each
mode provides different functionality associated with specific processes
in BVA analysis.
Spot Map Table
The Spot Map table is used to set up images for spot matching and
statistical analysis. The table lists data related to the Spot Maps
imported from DeCyder Differential Analysis Software DIA module.
Match Table
The Match table is used for the processes associated with inter-gel
matching. The table lists all data associated with the matching
algorithm.
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Protein Table
The Protein table is used to display and process data associated with the
protein spots identified across the gels. Each row of the table
corresponds to one protein spot, which may be present in several Spot
Maps.
Appearance Table
The Appearance table is used to display data associated with a selected
protein across the gels.
4.2 Protein quantitation
When using DeCyder Differential Analysis Software BVA for inter-gel
analysis it is highly recommended that an internal standard is used in
the experimental design (see Section 1.4). As discussed in the previous
section, the DIA quantifies the spots as a function of the internal
standard. This value is used in the BVA for analyses facilitating direct
comparison of spot maps, and is referred to as the standardized
abundance. The log10 of this value (referred to as standardized log
abundance) is used to aid scaling in graphical representations and is
employed in all statistical analyses.
If an internal standard is not used in the experimental approach
DeCyder Differential Analysis Software can express spot volumes of
specific proteins across gels as a ratio relative to the lowest volume spot
of that protein. This parameter is referred to as the abundance in BVA.
This latter approach is not recommended, since inter-gel quantitative
comparisons are not as accurate as the quantitation using an internal
standard. It is not possible to perform the BVA statistical functionality
with this non-internal standardized data.
As with the standardized abundance, log10 of this value is often used to
aid scaling in graphical representations (referred to as log abundance).
4.3 Creating and opening workspaces
From the Desktop or Start button menu select the DeCyder Differential
Analysis Software – BVA icon to open the module.
4.3.1 Creating workspaces
DeCyder Differential Analysis Software BVA requires XML files
generated in the DIA (section 3.7) together with the original scanned
image files, to create a new BVA workspace.
To load XML files in BVA select File:Create Workspace in the DeCyder
Differential Analysis Software BVA window or select the Create
workspace icon.
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Locate the XML files and select the files for opening by holding down
the Control key and clicking on the required files. Click Open.
Note: The XML files contain the necessary information to open the
associated image files automatically, providing the image and
XML files are in the same relative file path when processed in the
DIA module. If this is not the case, a message box will appear
giving the option to locate the image file(s). Click Yes and browse
to locate and select the corresponding image files. The newly
created workspace should now be saved (see section 4.13.5).
4.3.2 Opening workspaces
To open previously created and saved workspaces select File:Open and
browse to locate the required BVA file. When the file is located select
the file and click Open.
4.4 Viewing spot data
The general toolbar functions associated with the Image, 3-D and Table
View are identical to those in the DIA and are described in sections
4.4.1, 4.4.2 and 4.4.3, respectively.
4.4.1 Image View
Simultaneously displays two gel images. The gel image displayed can be
selected using the pull down menu in the title bar of each gel image
window.
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Properties
Properties associated with Image View are defined in the Image View
Properties window
Selecting View:Properties… or selecting the Properties icon and then
selecting the Image View tab displays the Image View Properties dialog
box.
Include in Image View: Select check box to display only the spots
displayed in the Table View.
Signature: Select to display the spot boundaries.
Annotation: Select to display user determined annotation as text boxes
on the gel Image Views. The annotation displayed is selected from the
list, only one category can be selected.
Selecting the Link Annotation check box results in spots derived from
different isoforms of the same protein (defined by the user by having the
same Protein ID, AC, name or comment) being grouped and labelled
with a single label. The Filter Annotation box can be selected to only
display annotations for pick status spots or spots selected as proteins of
interest.
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Match vectors when Match Table is displayed: Select to display the match
vectors (see section 4.6.2).
Link Image Views when scrolling: Select to link scrolling in both images.
Auto-center selected spots: Select to position selected spots in the center
of the Image View.
Picking References: The radius of the picking reference markers is
entered in the dialog boxes.
4.4.2 3-D View
A three dimensional representation of the two images selected in the
Image View, localized on the selected spot.
This view is not visible in the Spot Map mode. With the exception of
the data displayed below each of the 3-D images, the BVA and DIA
3-D Views are identical (see section 3.4.2).
A colored banner across one of the edges of the 3-D View images
corresponds to the color of the title bar in the corresponding image
view. The banner reveals the orientation of the 3-D View image,
denoting the edge of the view closest to the top of the gel.
Pick locations, when applicable, are displayed in the 3-D View (see
section 5.6).
Properties
Properties associated with the 3-D View are defined in the 3-D View
Properties window.
Selecting View:Properties… or selecting the Properties icon and then
selecting the 3-D View tab displays the Image View Properties dialog
box.
The size of area displayed in the 3-D View can be altered by entering a
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positive integer between 3 and 80.
The Show caption colors check box allows the colored banner indicating
the upper edge of the 3-D View (as seen in the Image View) to be
removed.
4.4.3 Table View
The contents of the Table View is dependent on BVA mode selected.
Similarly the editable properties associated with the Table View are
dependent upon the mode selected and are discussed in detail in later
sections. The data within the table can be sorted into ascending or
descending order by clicking on the column headers of the table.
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4.5 Defining spot map attributes
All spot map attributes are assigned in the Spot Map Table mode. Select
View:Spot Map Table or select the toolbar icon to work in the Spot Map
Table mode (if a new BVA workspace has been created the interface will
automatically open to the Spot Map Table). This mode consists of an
Image, Table View and Experimental Design View.
4.5.1 Function assignment
Spot Maps can be assigned with up to four functions that indicate the
role of the Spot Map in the BVA processing. The Spot Map functions
are:
Analysis (A) – Spot data is included in quantitative analysis. Those that
should not be assigned as analysis images may include preparative gels
for spot picking and images to be used as a template.
Master (M) –One spot map can be assigned as the Master Spot Map in
each workspace. All other Spot Maps are matched to the Master. When
creating a new workspace, the Spot Map with the largest number of
spots is automatically set to be the Master Spot Map.
Template (T) - One spot map can be assigned as the Template spot map
in each workspace. Protein ID, Protein AC, Name, Comment, pI, Mw
and Protein of Interest assigned to the spots of the Template Spot Map
will be cross annotated to matching spots in all the other images.
Pick (P) – This function should be assigned to a spot map if, in DIA, pick
assignments have been made and the user wishes to transfer the pick
status to corresponding spots in the BVA workspace. (see section 5.5).
To assign the spot map functions select View:Table View or click on the
Magnify Table View icon to display the Table View only. The table shows
the images that have been loaded into the workspace and the number
of spots that have been detected on them.
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Note: A set of images from the same gel will have the same number of
spots since the DIA detection algorithm is designed to detect the
same number of spots on images from the same gel.
In the Function column all the images are, by default, assigned Analysis
(A) and one of the analysis images is assigned as Master (M). The analysis
function designation indicates that each of the images will be included
in the statistical analysis.
The Master function can be assigned to a different image, by selecting
the image to be assigned as master using the left mouse click, then
selecting the Master check box at the bottom of the workspace in the
area entitled Spot Map Functions.
The Master function can be assigned to a different image, by selecting
the image to be assigned as master using the left mouse click, then
selecting the Master check box at the bottom of the workspace in the
area entitled Spot Map Functions.
All other function assignments are similarly performed.
4.5.2 Group assignment
A group is a collection of spot maps that for the purposes of the
experiment cannot be broken down into further sub-groups. Groups
can include analysis sets such as control groups, treated groups, time
points and temperature points. This spot map assignment is necessary
to facilitate the inter-group statistical analyses in DeCyder Differential
Analysis Software.
The internal standard (non-experimental group) spot maps are assigned
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as standard if the word standard, pooled or std is present in the image file
name. By default non-standard spot maps are assigned as
“Unassigned”.
The experimental group assignment is displayed in Experimental
Design View when within the Spot Map Table mode.
Spot Maps are located in the appropriate folder on the left side of the
Experimental Design view. The Spot Maps can be viewed by selecting
the desired group folder.
Spot Maps can be assigned to the predefined “Group1” and “Group2”
by selecting the Unassigned folder, then dragging and dropping Spot
Maps from the center panel of the Experimental Design view to the
desired folders.
The names of predefined “Group1” and “Group2” experimental
groups can be renamed by selecting the Edit Name button on the right
of the Experimental Design view.
New custom named groups can be generated. To create new groups
select Add, then type in the desired name of the group in the Group box.
A new group folder is subsequently displayed.
The assigned groups are also displayed in the Spot Map Table in the
Group column.
The description text box adjacent to the Group assignment can be used
to enter user defined information regarding the description of the
experimental group. This description is linked to the experimental
group.
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4.5.3 Comment assignment
The comment text box on the right of the data control panel can be
used to enter a user defined comment that is specific to the Spot Map
in the primary view.
4.5.4 Spot Map Table Mode
The Spot Map Table Mode displays data associated with the spot maps.
This data is derived from the XML file generated in the DIA module.
No.
Status
Image
Type
Label
No. Of Spots
Matched
Function
Group
Group Description
Condition 1
Condition 2
Sample ID
Comment
Spot map number in table
Indicates whether the image is Matched, Pending
or Processing.
Gel image file name.
Type of labelling chemistry used.
Dye used to label the protein (e.g., CyDye DIGE
Fluor Cy2, Cy3 and Cy5 minimal dyes).
Number of protein spots identified in the DIA.
Number of protein spots that have been matched
to the protein spots on the master.
Spot Map function (e.g. Master, Template,
Analysis and Pick).
Spot Map group (e.g. Control, Treated and
Standard).
User defined description of group.
User defined condition assigned numerically
(example Time point 1,2,3…).
User defined condition assigned numerically
(example Dose point 1,2,3…).
User specified identification of sample
User defined comment on Spot Map.
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4.5.5 Spot Map Table properties
Properties associated with Spot Map Table View are defined in the Spot
Map Table Properties window
Selecting View:Properties… or selecting the Properties icon then selecting
the Spot Map Table tab displays the Spot Map Table Properties dialog
box.
Table Column Order and Visibility: The column titles selected will be
displayed in the Table View. Dragging the column titles in the list
defines the order of the columns in the Table View. Clicking Default
restores the original settings.
Condition Labels: Labels for condition 1 and condition 2 (e.g. time, dose)
can be entered (see section 4.8.8).
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4.6 Inter-gel matching
All spot maps are matched to a master image sequentially. The spotmatching algorithm is a pattern recognition algorithm that matches one
single spot in one gel to a single spot in another gel based on it’s
neighboring spots.
4.6.1 Landmarking
Landmarking allows the user to manually define matched protein spots
in order to improve the accuracy of the gel-to-gel matching process. In
some cases images from different gels may vary sufficiently to require
landmarks to be set.
Landmarking is performed in the Match Table mode. Select View:Match
Table or select the toolbar icon to work in the Match Table mode. This
mode consists of an Image, 3-D and Table View.
To use a spot as a landmark the spot must be present on both the match
image and the master.
Click on the Magnify Image View icon to display the gel images only. The
image on the left represents the master image (in the Match Table mode
the left gel image is always the master image) while the image on the
right represents one of the images to be matched to the master.
To commence setting landmarks click on the button entitled Landmark
Mode, in the data control panel at the bottom of the workspace (this
button will now become yellow or to a color the user has assigned as
the landmark color).
In the drop down menu in the Image View title bar select the image for
landmarking.
If all the spots are unmatched the spot boundaries are red to indicate
this. If the spot boundaries are green in an unmatched set of spots then
the images are from the same DIA file (thus landmarks are not
necessary).
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If the spots on the images are not clearly visible it may be necessary to
alter the contrast/brightness settings of the images.
Click on the Contrast/Brightness icon and alter the position of the bars to
alter the contrast and brightness of the images until only the most
intense spots are visible. To set a landmark click on a clearly defined
spot on the master image (the spot boundary should become magenta color set as default). Now select the spot on the match image which
corresponds to the master image spot, so that this spot also becomes
magenta. A few seconds later a vector line, showing the displacement
of the spot, should appear showing that the spots have been matched
and the landmark has been set.
Note: Landmarking can be aided by viewing the pattern around the
selected spot in the 3-D View to confirm that the two selected
spots do correspond to each other. The number of spots being
displayed around the selected spot in 3-D can be altered by
opening the Properties window and selecting the 3-D View tab.
Altering the numerical value in the Spot Margin For Displayed Spot
option adjusts the parimeter size which may consequently
change the number of spots displayed around the selected spot.
It is recommended that landmarks be evenly distributed across the
image as this aids the matching process. Multiple gels can be
landmarked in an identical manner. Images from the same DIA file as a
previously matched gel are automatically landmarked. It is therefore
only necessary to set landmarks in one image from a set of images
derived from a single DIA analysis. Ideally the landmarked image
should be the standard spot map. The landmarks which were set on
previous spot maps will be colored yellow on the Master image, thus
the same landmarks can be set on the next match image by finding the
spots which correspond to the yellow master image spots. It is not
necessary to set the same landmarks on each gel, but as landmarks
chosen by a user tend to be the clearest spots, it is useful to have these
highlighted so that the landmarking process is speeded up.
When landmarking is completed deselect the Landmark button.
Note: It is usually only necessary to set landmarks on those images that
differ significantly from the Master image. In addition
landmarks may have to be set after matching if there are a high
number of wrongly matched spots, in which case matching will
have to be repeated. Often landmarks do not need to be set at all.
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4.6.2 Matching
The matching process is divided into two steps (Level 1 and Level 2).
1
The algorithm first matches large spots spread evenly over the
whole gel. The algorithm matches by comparing positions and sizes
of the neighboring spots. The matched spots are then used as
landmarks, equivalent to manual matches. The algorithm then
starts from these landmarks and matches neighboring spots. In this
way the matching propagates out from the landmarks by matching
a spot and then matching its neighbors.
2
Matching is performed by transforming the detected spot center
from one gel to the other gel. A spot center transformation is
considered a match if the transformed co-ordinates are close
enough to a spot center on the other gel. After each matching step
is a control that removes obvious mismatches.
To perform matching select Process:Match… or click the Match icon.
Select one of the different options to match all the images or just a
specific set of images.
Match all: matches all spot maps in the workspace (including those
already matched).
Match Primary: matches spot map selected as primary in the Image View.
Match pending and landmarked: matches spot maps that have not been
matched and those that have been landmarked after a previous
matching process.
Click Match to commence matching.
4.6.3 Match confirmation
The matches in the Table View are of two types, Auto Level 1 and Auto
Level 2. By default the two types of matches are displayed differently in
the Image View. Auto level 1 and Auto level 2 spots are represented by
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a darker and lighter spot boundary highlights, respectively. The
boundaries of the unmatched spots are colored red. The vector lines in
the Image View indicate the positional difference for the same protein
spot on different gels.
The matching can be checked within the Match Table mode to ascertain
the accuracy of the matching process. This can be done in several ways:
1
Confirm a few matches from different areas on the gel by clicking
around the image then moving onto the next image (recommended
option).
2
Order the spots in the Match Table by match quality values then
confirm the spots with the highest match quality values (see section
4.6.4).
3
Confirm randomly selected auto level 1 (e.g. 5) and auto level 2
(i.e. 10) matches by scrolling down the table.
4
Confirm all matches on every gel (time consuming and not
recommended).
Note: Areas of incorrect matching can be identified rapidly by
examining the match vectors displayed in the Image View. The
vectors should be orientated in a similar direction across the gel.
If an area of the gel does not follow this pattern (e.g. the vectors
are perpendicular or cross), it is highly likely that mismatches are
present in that area and should be looked at closely.
Deciding whether a match is accurate can be aided by viewing the
selected spot and the surrounding cluster in the 3-D View as well as
looking at the matched spots in the Image View. If the match is correct,
confirm the match, by clicking the Confirm match button in the data
control panel.
If the match is incorrect, click on the Break match button, which now
becomes Add Match. Click the correct spot on the match image.
If the spot on the match image has also been wrongly matched break
this match as before. Select the corresponding spots on the master
image and the match image and click on the button entitled Add Match.
If a matched spot on the primary image is not present on the master for
any reason it can be added to the master by clicking the Add to Master
button.
A comment can be appended to each match in the comment text box.
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4.6.4 Match Quality Metric
Match quality values are typically between 1 and 15, expressed as real
numbers approximated to two decimal points. This value represents
morphological, but not positional similarities in matched spots. The
match quality is calculated for only the internal standard spots that are
matched across a set of gel images, as we know that these proteins
should be the same.
The match quality value is calculated from the surface profile of each
internal standard on each gel matched to the master spot maps. The
surface profiles of all standard spots in a set of matched spots are
combined to form an “average” surface profile. Subsequently, all the
individual internal standard spot surfaces in the match are compared to
this “normal” surface (including the spot derived from the master spot
map). The quality score is the calculated difference between the
individual matched spots and the “normal” spot.
The spot surfaces that deviate a great deal from the “normal” surface
receive the highest score, whereas the spot surfaces that have close
resemblance to the “normal” surface will be given the lowest score.
Therefore, values approaching zero indicate good spot similarity and
hence accurate matching. As the quality score increases the degree of
similarity decreases revealing incorrect matches. The match quality
value in the Match Table can therefore be used to rapidly identify
incorrect matches when reviewing inter-gel matching.
In addition to quality score for each matched spot being displayed in
the match table, each set of matched spots (a protein) are also assigned
a global match quality score (displayed in the Protein table). This
protein match quality score represents the spot within the matched
group that has the highest quality score. This protein match quality
value enables the identification of poorly matched spots, thereby
highlighting matched spots in the protein set that may contain outliers.
All the match quality values for all matched spots in a protein can be
viewed simultaneously in the Appearance Table facilitating the rapid
identification of the outlying data point.
There is also a quality score value designated for each spot map, which
is displayed in the Spot Map Table. This value refers to the average
quality score for all matched standard spots on each gel. Hence, gels
that are poorly matched possess high match quality values. High spot
map match quality values are often due to poorly run gels that cannot
be matched efficiently without land marking (see section 4.6.1).
Note: The match quality score is based exclusively on the surface
profile of the spots on the internal standard spot maps.
Therefore, the quality score cannot be calculated for
experimental designs that do not contain an internal standard.
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Furthermore, each co-detected spot for multiple images on the
same gel all possess the same match quality value, which is
associated with the profile of the internal standard spot only.
4.6.5 Spot Merging
Very occasionally, a protein peak will be dissected into several spots
despite appearing as a single discrete spot. This is normally due to
subtle gradient changes on the spot surface that are not always
apparent to the naked eye. These “split” spots can be merged to form
a single spot boundary within the Match Table mode.
Select the matched spot in the image view that requires to be merged to
surrounding spot(s).
Selecting Edit:Merge Spots... results in the surrounding unmatched spots
being exclusively displayed. Click on the spot in the image view that
requires merging to the spot selected originally.
Note: Two spots can be merged if at most, one of them is matched to
another spot. If one master spot is matched to several spots, it's
possible to merge it with another spot, but only if that other spot
is not matched.
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Both the image view and the 3-D View display the new spot boundary
showing all the component spots. The volume of the merged spot and
subsequent volume ratios (when using an internal standard) are
recalculated automatically.
The merging process can be reversed by selecting Edit:Split Previously
Merged. Splitting merged spots results in all the associated spots being
unmatched.
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4.6.6 Match Table
The Match Table displays spot specific information.
Pos.
Master No.
Status
Master
Match
Type
Comment
Match Table row number
Protein spot number on Master Spot Map
Match confirmation status (confirmed or
unconfirmed)
Protein spot co-ordinates on Master Spot Map
Protein spot co-ordinates on Match Spot Map
Types of match:
Unmatched: Unmatched spots
Auto Level1: Spots matched after first level of
matching
Auto Level2: Spots matched after second level of
matching
Manual: Spots matched by the user
Added: Spots that have been added to the master
User defined comment regarding matched protein
spots.
4.6.7 Match Table properties
Properties associated with Match Table are defined in the Match Table
Properties dialog box.
Selecting View:Properties… or selecting the Properties icon then selecting
the Match Table tab displays the Match Table Properties dialog box.
Include in Match Table: Selects the categories of spots that are to be
displayed in the Match Table.
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4.7 Graphical representation
The graphical view in DeCyder Differential Analysis Software BVA
allows the user to view scatter plot representations of data points
associated with individual proteins. For example the manner in which
the expression of a protein changes with time or drug dosage can be
graphically viewed. The graphical views can only be seen in the Protein
Table mode and Appearance Table mode. Examples of the graphical
representations are illustrated in the next section.
Properties associated with graph view are defined in the Graph View
dialog box.
Selecting View:Properties… or selecting the Properties icon then selecting
the Graph View tab displays the Graph View Properties dialog box.
Parameter visualization
Allows the user to define how data is displayed on the graph by
designating the parameters displayed on the X and Y-axis of the graph.
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The X-axis options allow the user to display data according to group,
conditions or fluor type. The sequence of the groups on the X-axis are
determined by the order of group folders in the Experimental Design
view within the Spot Map Table mode. This can be changed by
dragging and dropping the folder into the desired order within the
Experimental Design view.
The Y-axis options allow the user to display either the abundance, log of
the abundance, standardized abundance or log standardized
abundance. However the statistical functions within DeCyder
Differential Analysis Software only utilize log standardized abundance.
Therefore the graphical representation of this parametric value only
reflect the data points used in the statistical analyses.
Dashed line options allow the user to display dashed lines on the scatter
plot linking data points by the specified association. Samples either
derived from the same gel or possessing the same sample ID can
therefore be easily identified in the graph view.
Include in graph view
The options available determine the data points plotted on the graph.
By selecting the Spot check box all proteins spot data points associated
with the user defined X-axis parameter are shown. Colored circles
represent these data points. The colors representing the groups can be
assigned in the Experimental Design view.
Selecting the Mean Value Crosses check box results in the mean value of
the data points associated with the user defined X-axis parameter being
displayed (represented by a cross). Lines connecting these mean values
can be displayed by selecting the subsequent check box.
If either Groups or conditions are assigned to the X-axis, the Y-axis
values corresponding to the internal standard can also be displayed by
selection of the Standard check box.
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Note: The DeCyder Differential Analysis Software standardization
process defines all protein abundance values associated with the
internal standard to be 1 (or zero if the log values are displayed).
For further details see Appendix C.
The legend check box allows the user to determine whether legends
defining the color coding of groups will be displayed.
Y axis settings
The Y-axis setting options enable the user to change the Y-axis scaling:
• Automatic allows optimization of the Y-axis scaling.
• Manual allows the user to define the maximum and minimum
values on the Y-axis.
• Manual with Automatic increase allows the user to define the
maximum and minimum values on the Y-axis but adjusts the Y-axis
values if the data points fall outside the manual defined values.
4.8 Protein statistics
4.8.1 Opening the protein statistics dialog box
DeCyder Differential Analysis Software BVA possesses several
statistical analysis methods that can be employed to ascertain whether
changes in expression of specific proteins are significant between
samples from different experimental groups.
To open the statistical analysis dialog select menu Process:Protein
Statistics.
Alternatively, the statistical analysis dialog can be opened using the
toolbar icon.
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The dialog box contains various options allowing statistical analysis of
protein data from spot maps loaded in the DeCyder Differential
Analysis Software BVA.
4.8.2 Defining groups
A group in BVA is a collection of spot maps that for the purposes of the
experiment cannot be broken down into further sub groups. For
example, 3 replica gels from one sample are considered one group.
Alternatively, 3 gels of 3 different samples treated with exactly the same
experimental conditions can also be considered as a group. There is no
limit on the number of groups that can be assigned in DeCyder
Differential Analysis Software BVA.
The different experimental groups must first be identified in DeCyder
Differential Analysis Software BVA in order to undertake statistical
analysis of protein expression changes (see section 4.5.2).
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4.8.3 Overview of Statistical tests
The BVA module has four “principle groups comparison” or
“experimental condition comparison” methods that can be applied to
analyze protein spot data.
• Average ratio between two groups or two populations of groups.
• Student’s T-test: Statistical analysis between two groups or two
populations of groups.
• One-Way ANOVA (ANalysis Of VAriance): Statistical analysis
between all groups.
• Two-Way ANOVA: Statistical analysis between the two conditions
in an experimental design where there are two independent factors
(e.g. time-dose study). This analysis allows the internal and mutual
effects of the two factors to be quantified.
The log standardized abundance is the only variable subjected to the
above statistical analyses within DeCyder Differential Analysis
Software BVA. The standardized abundance is derived from the
normalized spot volume standardized against the intra-gel standard.
The log of the standardized abundance values are used in order that the
data points approach a normal distribution around zero, thereby
fulfilling the requirements of the subsequent statistical tests.
Consequently, the statistical analysis functionality is not valid unless
the experimental design includes an internal standard on every gel.
General requirements
All spot maps included in the analysis need to be co-run with an
internal standard.
Only Spot Maps assigned as “Analysis” in the Spot Map Table are
included in the statistical analysis.
Automatic recalculation
Protein Statistics are recalculated automatically if any data that affects
the statistics are changed, e.g. if a match is broken or if a spot map is
assigned to a new group.
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4.8.4 Independent and paired analyses
The Student’s T-test, One-Way ANOVA and Two-Way ANOVA can be
either independent (normal) or paired (individual). Both types are used
to test the hypothesis that a variable, in this case protein abundance,
differs between groups or experimental conditions.
The paired test is specifically used when each data point in one group
corresponds to a matching data point in the other group(s). A typical
example would be the same group of patients before and after a
treatment. The unpaired tests are more general techniques that can be
used to test whether standardized protein abundance differs between
groups, and does not require that the groups be paired in any way, or
even of equal sizes. The following graphs are examples illustrating
independent and paired tests.
Independent test. Abundance of a specific protein in five diseased
individuals compared to a group of five non-diseased individuals. Data
points are independent since there are different individuals in each
group.
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Paired test. Abundance of a specific protein in the above five diseased
individuals before and after drug treatment. Data points are paired
because the same individuals are present in pre- and post-treated
groups. Although there is no significant difference between the means
of standardized abundance in the two groups, a paired T-test reveals
that the two groups are significantly different, since each individual
exhibits an increase in protein abundance after drug treatment. In this
example, there is a single result (i.e. one image) for each individual in
each group. Experiments employing replicate images from the five
individual scan also be applied to a paired test. In this instance the
means of the replicas are calculated then subjected to pairing (ensuring
the pairs are independent).
Note: The above graphs are conceptual (not generated in DeCyder
Differential Analysis Software).
In order to perform paired testing, the pairing of spot maps must be
pre-assigned in the Spot Table view. This can be done by inserting a
numerical identifier in the text box labelled Sample ID at the bottom of
the Spot Table view. For example, spot maps of protein samples from
individual 2 in each the of groups can be assigned. In this way the
dependency can be associated with each spot map.
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4.8.5
Student’s T-test & Average ratio
Average ratio
Log standardized protein abundance is the only variable analyzed in the
Student’s T-test within DeCyder Differential Analysis Software BVA
module. The degree of difference in the standardized abundance
between 2 protein spot groups is expressed as the average ratio. The
average ratio value indicates the standardized volume ratio between the
two groups or populations. Values are displayed in the range of
-∞ to –1 for decreases in expression and +1 to +∞ for increases in
expression. Values between -1 and 1 are not represented, hence a
two-fold increase and decrease is represented by 2 and –2, respectively
(not 2 and 0.5).
Note: The average ratio parameter is displayed in this manner but the
statistical analyses are based on the log of the true ratio
measurement.
Student’s T-test
Student’s T-test, often known simply as the T-test, is one of the most
commonly used of all statistical tests. The Student’s T-test is used to test
the hypothesis that a variable differs between two groups. The Student’s
T-test performed is an equal variance two-tailed test, therefore,
direction of change (i.e. increases and decreases) in the standardized
abundance parameter are considered.
The T-test requires a minimum of two data points in each of the two
groups. Greater statistical validity can be achieved using larger number
of replicates. It is recommended that the largest possible number of
biological replicates are performed for optimal validity. However, if
biological replicates are not possible, gel replicates can be used for the
purposes of the statistical analysis. In these instances it is recommended
that gels be run, at least, in triplicate, hence each group has a minimum
of three data points.
Null hypothesis
The Student’s T-test null hypothesis is that there is no change in the
protein abundance between experimental groups (i.e. the average ratio
between two groups is 1). Therefore the T-test p value (seen in the
T-test column of the protein table) represents the probability of
obtaining the observed data if the two groups have the same protein
abundance. For example, if the T-test p value between two groups is
0.01, then the probability of obtaining the observed difference in
protein abundance by stochastic variation alone is 1 in a 100.
Protein abundance differences are generally assumed to be statistically
significant when p≤0.05. However a critical value of 0.05 would mean
that 5% of the data points would be expected by stochastic events alone
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(e.g. 50 spots out of 1000 tested). It is therefore advisable to review the
critical value applied to the data. Due to the small amount of
experimental variation within Ettan DIGE system and subsequent
DeCyder Differential Analysis Software analysis, a critical value of 0.01
is often applied.
Assumptions and limitations
The main assumptions of the Student's T-test are the following:
• Log standardized abundance values within a group are normally or
approximately normally distributed. When the assumption of
normality is violated, the T-test tends not to perform well when the
sample size is small, or the significance level is small (e.g. p<0.01).
However, two-tailed tests are surprisingly robust even for skewed
data.
• The parametric variances of the two groups are equal. However,
the test tends to be robust when the sample sizes are approximately
equal. If the sample sizes are not equal, then the worst case occurs
when the smaller sample comes from the population with the larger
variance.
Performing analysis
The user can perform T-test calculations and average ratio calculations
between any two groups (or populations of groups).
1
In the Protein Statistics dialog box, select the option button to
indicate whether the T-test is independent or paired. Pairing of spot
maps has to be pre-assigned in the Spot Map Table mode (see
section 4.8.4) for paired testing.
2
Select the two groups in the in the population 1 and population 2
lists (population of groups can be selected by ctrl + left mouse
clicking the selected groups). Select the Average ratio and the
Student’s T-test check boxes. Click Calculate.
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The T-test p values and average ratios will be displayed in the
protein table.
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The spots in the Protein Table can then be sorted by clicking on the
T-test column header. This will order the table so that the protein
spots exhibiting the most significant changes are listed at the top of
the table. Since the T-test is two-tailed the statistical significance of
both increases and decreases in protein abundance quantified are
ordered together. A positive average ratio value indicates an
increase from population 2 to population 1 (the order is stipulated
when defining groups in the protein statistics dialog box).
Conversely a decrease in abundance is denoted by a negative
average ratio.
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Specific Requirements
If there are at least two members in each group, T-tests and average
ratios will be calculated. If there is only one member in each group only
the “average” ratio will be calculated.
4.8.6
ANOVA
Overview
Analysis of variance (ANOVA) is a family of methods that can be used
to analyze the results from both simple and complex experiments. It is
one of the most important statistical tests available for biologists and at
its lowest level it is essentially an extension of the logic of Student’s
T-tests to those situations where the concurrent comparison of the
means of three or more samples is required. Thus, when comparing two
means, the ANOVA will give the same results as the T-test for
independent samples (if comparing two different groups or
observations). There is no restriction on the number of groups that can
be analyzed. It is equally valid for testing differences between 2 groups
or among 20.
There are two types of ANOVA analyses in DeCyder Differential
Analysis Software BVA module, One-Way and Two-Way. The One-Way
ANOVA evaluates differences in all assigned groups, whereas the TwoWay ANOVA can also evaluate the statistical significance of effects
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from independent factors (such as time, temperature and dose). These
are entered as conditions 1 and 2.
The ANOVA tests require a minimum of two replicate data points.
Greater statistical validity can be achieved using larger number of
replicates. It is recommended that the largest possible number of
biological replicates are performed for optimal validity. However, if
biological replicates are not possible, gel replicates can be used for the
purposes of the statistical analysis. In these instances it is recommended
that gels be run, at least, in triplicate, hence each group has at least
three data points.
As with the Student's T-test the ANOVA tests can be either independent
or paired. Paired ANOVA tests require dependencies to be applied (see
section 4.8.4).
Null hypothesis
The ANOVA test null hypothesis is that there is no change in the
protein abundance between any of the experimental groups analyzed.
The ANOVA test compares the variance between groups with the
variance within groups. The ratio of the between-groups variance to the
within-groups variance is known as the F ratio, and can be used to
generate a p value. If the F ratio is large, then it shows that the variation
between groups is large compared with the variation within groups,
and thus that the groups may be different. Therefore the p value
(displayed in the ANOVA column of the protein table) is a measure of
the probability of obtaining the observed data if the groups have the
same protein abundance. For example, if the ANOVA p value between
two groups is 0.01, then the probability of obtaining the observed
difference in protein abundance by stochastic variation alone is 1 in a
100. Protein abundance differences are generally assumed to be
statistically significant when p ≤ 0.05. However a critical value of 0.05
would mean that 5% of the data points would be expected by
stochastic events alone (e.g. 50 spots out of 1000 tested will be false
positives). It is therefore advisable to review the critical value applied
to the data. Due to the small amount of experimental variation within
Ettan DIGE system a critical value of 0.01 is often applied.
Assumptions and limitations
The main assumptions of the ANOVA tests are the following:
• The populations from which the samples were obtained are
normally or approximately normally distributed.
• The variances of the populations must be equal.
There is a certain amount of leeway in both these assumptions,
particularly the first: small departures from a normal distribution are
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unlikely to be of any consequence. However, if the variance among the
groups differs greatly, the ANOVA p value may have poor validity.
4.8.7
One-Way ANOVA
Overview
The simplest form of ANOVA is known as one-way ANOVA, and in
DeCyder Differential Analysis Software is used to test for differences in
standardized abundance. For example, a clinical trial might consist of
three groups of patients according to whether they were given drug A,
drug B, or placebo. ANOVA could be used to test the null hypothesis
that there is no difference in standardized protein abundance among
the groups.
The test will not indicate which groups are different from which other
groups, just that there is an overall difference. In the above example, a
significant difference does not necessarily mean that drug A is different
from drug B, or that either drug is different from placebo. All a OneWay ANOVA test indicates is that, in someway, the patients are affected
by one of the drugs.
Performing analysis
1 In the Protein Statistics dialog box, select the option button to
indicate whether the test is independent or paired. A paired
ANOVA test is called a repeated measures ANOVA (RMANOVA).
Pairing of spot maps have to be pre-assigned in the spot map Table
View (see section 4.8.4) for paired testing.
2
Select the One-Way ANOVA check box (all assigned groups are
included in the One-Way ANOVA test). Click Calculate.
3
Select the Properties icon on the tool bar and ensure that 1-ANOVA
check box is selected in the Spot Map Table tab. If the One-Way
ANOVA analysis is paired, select the Paired tests option button. A
repeated measures (paired) One-way ANOVA will then be
automatically calculated. Click Calculate to perform the analysis.
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The One-Way ANOVA p values will then be displayed in the protein
table. As with the T-test analysis, spots can be ordered by significance
by clicking on the 1-ANOVA column heading in the protein table.
Specific requirements
All groups that have at least two members will be included in the
calculation. To analyze a subset of groups, those that are to be excluded
have to be de-selected as analysis in the Spot Map function tab (see
section 4.5.2).
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4.8.8
Two-Way ANOVA
Overview
Two-Way ANOVA calculates the significance of the difference between
groups with the same condition 2 and different condition 1 value
(Two –Way ANOVA Condition 1) and the other way around
(Two –Way ANOVA Condition 2). The Two-Way ANOVA analysis
also calculates a significance value of the mutual effect of the two
factors (Two –Way ANOVA Interaction).
There are therefore three sets of hypothesis with the Two-Way
ANOVA. The null hypotheses for each of the sets are given below.
The population means of the first condition are equal. This is like the
One-Way ANOVA for condition 1 exclusively.
The population means of the second condition are equal. This is like the
One-Way ANOVA for condition 2 exclusively.
There is no interaction between the two factors. A significant ANOVA
Interaction value indicates that the two factors affect each other due to
synergy or interference.
Examples of such effects are illustrated on next page:
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Graphical examples of Two-Way ANOVA analyses.
Graphs illustrate changes in protein abundance (y-axis) for a twocondition experiment. Condition 1 (x-axis) represents two
temperatures A and B; condition 2 (red and yellow circles) represents
drug treated and control samples. Each condition is in triplicate, hence
there are four experimental groups with 3 samples in each group.
Conditions 1 and 2 are used to link groups together based on one
common factor, i.e. group 1 and 2 may have the same condition 1 value
(both temperature 1) but different condition 2 value (drug treated or
control). Groups 3 and 4 will have the other condition 2 value (both
temperature 2) with different condition 2 values (drug treated or
control).
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• Experiment 1, there is no variation in abundance from temperature
A to B or from treated and non-treated samples.
• Experiment 2, temperature B results in increased protein
expression whereas drug treatment has no effect.
• Experiment 3, drug treatment results in increased protein
expression whereas temperature changes have no effect.
• Experiment 4, both drug treatment and increasing temperature
result in increased protein expression i.e. the two conditions are
independent and P12 is not significant.
• Experiment 5, drug treatment only in conjunction with change in
temperature results in an increased protein abundance i.e. the two
conditions are synergistic hence P12<0.001.
P1, P2 and P12 are labelled as 2-ANOVA-“condition 1”,
2-ANOVA-“condition 2” and 2-ANOVA-interact in the Protein Table.
Defining conditions
In order to perform Two-Way ANOVA analysis the experimental
conditions for each gel image must first be assigned. Prior to this the
condition labels can be defined by selecting the properties icon and
selecting the Spot Map Table tab, the condition label can then be entered
into the appropriate text boxes.
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The experimental conditions of each gel image can then be entered at
the bottom of the spot map view. Conditions can be entered as integers
to represent the exact conditions (e.g. dose 1,2,3…n can represent a
series of 5 mg increments in a drug treatment). Up to two conditions
can be designated for each spot map.
Note: It can, therefore, be seen that when assigning a two condition
experimental design that the Condition labels must be defined
for a Two-Way ANOVA test. When assigning a single condition
experiment the data is assigned into Groups for One-Way
ANOVA testing.
Performing analysis
1 In the Protein Statistics dialog box, select the option button to
indicate whether the test is independent or paired. Pairing of spot
maps has to be pre-assigned in the Spot Map Table View (see
section 4.8.4) for paired testing.
2
Select the Two-Way ANOVA check box. Click Calculate.
3
Select the Properties icon on the tool bar and ensure that 2-ANOVA“condition 1”, 2-ANOVA-“condition 2” and 2-ANOVA-interact check
boxes are selected in the spot map tab. If the Two-Way ANOVA
analysis is paired, select the Paired tests option button. A repeated
measures (paired) Two-way ANOVA will then be automatically
calculated. Click Calculate to perform the analysis.
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The Two-Way ANOVA p values will then be displayed in the protein
table. As with the other analyses, spots can be ordered by significance
by clicking on the appropriate 2-ANOVA column headings in the
protein table.
Specific requirements
At least two experimental groups with at least two members in each are
required. The experimental design should also be set up so that the
following criteria are applied:
• Condition 1: The experimental groups should have both condition
values filled in, so that there are at least two groups with different
condition 1 values and the same condition 2 value.
• Condition 2: The experimental groups should have both condition
values filled in, so that there are at least two groups with different
condition 2 values and the same condition 1 value.
Additional requirement for paired Two-Way ANOVA:
• At least two different sample IDs in each group (spot maps with the
same condition 1 and condition 2 value).
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For further information on experimental design and set up examples,
see Appendix D, Experimental design and set up examples.
4.8.9 Further statistical analyses
The data generated within DeCyder Differential Analysis Software can
be exported as an XML file and then extracted using the XML toolbox,
enabling post processing of several parameters. The user can therefore
apply their own statistical analysis algorithms to normalized and prenormalized data to suit their own specific requirements.
4.9 Protein Filter
The protein filter is a tool that allows the selection of proteins based on
various user-defined criteria. To open the protein filter select
Process: Protein Filter or click the protein filter icon. The filter allows
proteins to either be given a Protein of Interest or Pick Status by selection
of the appropriate check box.
The general filter settings allows the selection of all detected proteins by
selecting the Select all check box. This panel within the filter also
permits the restriction of the filtering to only a subset population of
proteins either those confirmed or those present on a specified number
of spot maps.
Proteins can also be selected on the statistical parameters described in
section 4.8. The desired statistical analyses check boxes are selected and
the desired values can then be entered in the appropriate text boxes.
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Proteins can also be selected on the basis of their physical properties,
such as volume, X-co-ordinate and Y-co-ordinate. These physical properties
pertain to the spots on the Master spot map or the Pick spot map (if
present) depending on which is selected from the pull down menu.
Note: The use of the protein filter in selecting spots for picking is
further discussed in section 5.
4.10 Molecular weight (Mw) and isoelectric point (pI)
DeCyder Differential Analysis Software BVA provides the user with the
functionality to calculate and display the molecular weight (Mw) and
isoelectric point (pI) of the proteins on all the images being analyzed.
The Mw and pI of known proteins can be entered into the Protein Table,
these values (known as the base list) are then used to calculate the Mw
and pI for all the other proteins on the images.
4.10.1 Entering Mw and pI of known proteins
To enter the Mw and pI of the known proteins in your experiment select
the protein in the Protein Table (PT) and enter the Mw and pI into the Mw
and pI text boxes, in the data control panel. The Mw and pI entered for
a protein can be edited by clicking on the protein and changing the
values in the Mw and pI boxes.
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Calculating Mw and pI
To calculate the Molecular weight (Mw) and isoelectric point (pI) of all
the proteins on the images select Process:Calibrate pI and Mw or click the
Calibrate pI and Mw icon.
The subsequent dialog box, displays the pI and Mw values that have
been manually entered into the protein table, using the data control
panel in the Protein Table mode. These values are used as a base list to
calculate the pI and Mw values for all the proteins.
Calibration of the pI and Mw values can be user determined by
selecting Linear, Log Linear or Cubic Spline:
• Linear: Creates a straight line between all values in the base list.
• Log Linear: Similar to the linear function, except that the values in
the base list values are first logged (Log10). So the line is based on
logarithmic values.
• Cubic Spline: Creates a line using the cubic spline function, which
fits a curved line through all points in the base list.
The type of first dimension Isoelectric Focusing (IEF) strip influences pI
calibration used. For example, a linear Isoelectric Focusing (IEF) strip
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makes the Linear pI calibration method most suitable.
Mw calibration is influenced by the second dimension gel used. For
example, Log Linear Mw calibration method is most suitable for nongradient gels.
After choosing one of the three available options for calculating the pI
and Mw click OK to perform the calculation. The pI and Mw of the
proteins should now appear in the Protein Table.
Displaying the Mw and pI values on the images
To display the calculated pI and Mw values of the proteins on the gel
images, click on the Properties icon to display the Properties window. By
default the window should open on the Image View tab. Select the
Annotations check box then highlight the pI and Mw option from the list
of different annotations. Click OK to display these values on the images.
Use the Zoom in function to make the annotations more legible.
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4.11 User-defined protein labelling
4.11.1 Confirmation
Protein confirmation allows the marking of proteins for user reference
purposes (e.g. for visually checked proteins). No part of the analysis in
DeCyder Differential Analysis Software BVA demands confirmation of
proteins.
To confirm a protein, select the Confirm button in the data control panel.
If the selected protein is confirmed, it can be unconfirmed by selecting
the Unconfirm button.
4.11.2 Name
User-defined names can be entered through the text box in the data
control panel.
4.11.3 Comment
User-defined comments can be entered through the text box in the data
control panel.
4.11.4 Protein of interest
The Protein of Interest check box is selected for proteins that may
warrant further investigation by the user.
4.11.5 PTM
Protein spots possessing a post-translational modification (PTM)
identified by methods outlined in Ettan DIGE application notes (e.g.
application note 18-1170-83 AA, obtainable on the Amersham web
site) can be denoted by checking the PTM check box.
4.12 Database linking
DeCyder Differential Analysis Software provides the user with
functions that enable rapid access to both local and web based
databases through Protein IDs and Accession Numbers (AC). This
functionality is designed to aid further study of identified proteins using
information from existing databases. A list of web based databases are
provided as default in DeCyder Differential Analysis Software. The
linking uses the default web browser to interact with the databases. The
Protein ID or AC is substituted into the web address of the database.
On condition that the protein ID entered in the protein table is in the
format that is recognized by the search algorithm of a database the user
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can directly access a selected protein from BVA.
In DeCyder Differential Analysis Software BVA module it is possible to
link proteins to information in databases
Use the Database tab of the BVA module property pages to set up how
Protein IDs and ACs are inserted in the web address of different
databases.
4.12.1 Adding databases
To add a database to the default list of databases in BVA, click on the
Properties icon (or select View:Properties) to open the Properties window
then click on the Database tab.
To add a database to the list click on the Add button.
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The name of the database is entered in the Label text box. The web
address (URL) in the format specified is entered in the address text box.
The address should contain a placeholder string, which will be replaced
by the protein’s ID or Accession Number (AC) depending on the string
is used and the information available.
There are three different placeholder strings:
• #BVA_PROTID# - The database will be available if there is
information in the Protein ID column of the Protein Table.
• #BVA_PROTAC# - The database will be available if there is
information in the Protein AC column of the Protein Table.
• #BVA_PROTIDAC# - The database will be available if there is
information in either the Protein ID or the Protein AC column of
the Protein Table.
Upon selecting a protein, only databases that have placeholder strings
that fit the information entered for the protein will be available.
To verify that the address is entered correctly, select a protein in the Test
pull down menu and click the Test button. If the address is correct, the
browser should open and information on the test protein should be
shown.
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Example
Database - SwissProt: AC or ID search
Address (with substitution string) - http://www.expasy.ch/cgi-bin/getsprot-entry?#BVA_PROTIDAC#
Protein AC - (Human myoglobin)- P02144
The web browser will open at the address:
http://www.expasy.ch/cgi-bin/get-sprot-entry?P02144
If a system for identifying proteins other than AC or ID is being used,
text can be entered manually or pasted into the test place holder string
box.
Accessing databases
Ensure that the required database is selected in the Properties window
by clicking on the Properties icon and selecting the Database tab. Select
the desired database(s) from the Database Selection list then click OK.
To access a database right mouse click the protein to be queried in the
Protein Table, then select the database. If both Protein ID and Protein
AC labels are available a choice of the label to be used is given. The web
browser is then opened at the appropriate address.
Note: The linking is dependent on the place holder string being
compatible with the selected database.
Removing and editing databases
Removing and editing are performed in the Database Properties dialog
box.
To remove a database, highlight the selected database and click the
Remove button.
To edit a database, highlight the selected database and click the Edit
button. The database can then be amended. Click OK to accept the
changes.
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4.13 Viewing protein data
Information relating to proteins (such as those described in the previous
four sections) can be viewed through the Protein Table and the
Appearance Table. The Protein Table displays several proteins
simultaneously, allowing rapid comparison of data from different
proteins. The appearance table displays a single protein showing data
from individual gels where the protein occurs.
Data can also be displayed by annotating protein spots in the gel images
view, which can be a useful tool for presentation of data.
4.13.1 Protein Table
The table below describes the information contained in the Protein
Table:
Pos.
Master No
Status
Protein ID
Protein AC
Appearance
Protein Table row number
Protein spot number on Master Spot Map
Confirmation status of protein
Protein identification for database linking
Protein accession number for database linking
Indicates the number of Spot Maps in which the
selected spot appears, and the function these
Spot Maps have
Example: “20(20) A, M” means that the spot is
present in 20 out of 20 Spot Maps. Furthermore,
the Spot Maps are all assigned to be used in
statistical analysis. The spot is also present on the
master image.
T-test
p value calculated using the Student’s T-test
Average ratio
Average ratio between the groups selected in the
protein statistics dialog box
1-ANOVA
p value calculated using One-way ANOVA
statistical test
2-ANOVA –
p value calculated using Two-way ANOVA
”Condition 1”
statistical test with respect to Condition 1
2-ANOVA –
p value calculated using Two-way ANOVA
”Condition 2”
statistical test with respect to Condition 2
2-ANOVA-Interact
p value calculated using Two-way ANOVA
statistical test with respect to interactions
between conditions 1 and 2
Picked
Indicates whether the protein spot has been
assigned for picking
Table continued on next page
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Pick spot volume
Function
pI
Mw
Name
Comment
Match Quality
PTM
Displays the volume of a spot designated for
picking after spot detection and background
correction. This value can be used as an
indication of whether a spot can be identified by
mass spectrometry
Proteins assigned Protein of Interest are marked
with an ‘I’ in this column.
Isoelectric point of protein (can be user-defined or
calculated)
Molecular weight of the protein (can be userdefined or calculated)
User defined protein names
User defined notes on proteins
Morphological similarity metric describing
deviation of internal standard spot to an “average”
internal standard spot in protein set.
Indicates that the protein has a post translational
modification.
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The columns displayed in the protein table can be changed using the
Protein Table Properties dialog box.
Protein Table Filter: Select check boxes to display only proteins of interest
or pick assigned proteins in the Image View and the Table View. Both
check boxes can be selected if there are spots selected as proteins of
interest and pick status.
Table Column Order and Visibility: The selected column titles are displayed
in the Table View. Clicking and dragging the column titles in the list sets
the order of the columns in the Table View. Clicking Default restores the
original settings.
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4.13.2 Appearance Table
The table below describes the information contained in the Appearance
Table:
No.
Image
Type
Label
Function
Abundance
Std Abundance
Volume
Peak Height
Group
Group Description
Condition 1
Condition 2
Sample ID
Comment
Match Quality
Spot map number
Gel image file name
Indicates the dye chemistry used
Indicates the CyDye DIGE Fluor minimal dye label
Spot map function (e.g. Master, Analysis, Pick and
Template)
Relative volume of all spots representing a
particular protein in a BVA data set, used when no
internal standard is present.
Protein volume calculated relative to internal
standard
Spot pixel volume (expressed background
subtraction)
Largest pixel value within the spot boundary
(expressed background subtraction)
Assigned spot map group
Description of spot map group
Condition 1 numerical value
Condition 2 numerical value
User defined sample identification
User defined comment
Morphological similarity metric describing deviation
of internal standard spot to an “average” internal
standard spot in protein set.
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The columns displayed in the appearance table can be changed using
the Appearance Table Properties dialog box (see next page).
Table Column Order and Visibility: The selected column titles are displayed
in the Table View. Clicking and dragging the column titles in the list sets
the order of the columns in the Table View. Clicking Default restores the
original settings.
4.13.3 Spot annotation
Displaying annotation
Various protein data elements can be displayed on the Image View. The
annotation can be defined using the Image View Properties dialog box.
See Section 4.4.1 for details.
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Positioning the annotations
The position of the annotations in the Image View can be altered to
ensure that the different annotations are clearly visible. This feature is
useful when one annotation obscures another annotation. To alter the
position of an annotation, select Edit:Move Annotations so that a tick
appears next to this option. The annotation labels in the Image View
can then be dragged to the new positions.
4.13.4 Customizing display colors
The colors used in the various views can be customized by selecting the
Colors tab in the Properties dialog box.
Match Colors: The spot boundary colors of the different categories of
spots displayed in the Match Table Image View can be changed by
clicking on the colored circles then selecting a new color.
Spot Colors: Spot boundary colors of the different categories of spots
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displayed in the Protein Table and Appearance Table Image Views can
be changed by clicking on the colored circles then selecting a new color.
Annotation Colors: The colors of the gel image annotation text boxes and
linking lines can be changed by clicking on the colored circles then
selecting a new color.
Spot Map Colors: The colors that designate different categories of spot
map in the Image Views can be changed by clicking in the colored
circles then selecting a new color.
The original settings can be restored by clicking Default.
4.13.5 Saving, exporting and printing
Save As
Workspaces are saved as BVA files, which contain all the data
associated with all the loaded spot maps. The BVA files do not contain
the image files but do contain information on their file path.
Select File:Save As, and browse to locate the folder to save the
workspace. Enter a file name then click Save.
Save
Save workspace updates the saved version of the file to include any
changes that have been made to the workspace.
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Printing
Select File:Print to open the Print dialog box.
Multiple check boxes can be selected for printing the required
workspace views.
DeCyder Differential Analysis Software supports the following
printers:
• HP LaserJet 4000N
• HP color LaserJet 5M
• HP 2500C
• HP DeskJet 895 Cxi
• HP DeskJet 950C
• HP Laser Jet 4100 DTN
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Using the clipboard
Any aspect of the different views within the workspace can be copied
to the clipboard for pasting into another application.
Depending on the BVA mode different views will be available for
copying. Click on the part of the workspace chosen for pasting then
select Edit:Copy (the part of the workspace selected is described in this
pull down menu). The clipboard can then be pasted into other
applications.
Exporting data
Data associated with the entire workspace can be exported in an XML
based format (DeCyder Differential Analysis Software XML) by
Selecting File:Export Workspace. The DeCyder Differential Analysis
Software XML file can then be opened in the XML Toolbox for further
analysis.
Pick list data can be exported as a text file by selecting File:Export Picking
List From:Primary Spot Map or Pick Spot Map. The text file can then be
utilized by Ettan Spot Picker.
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5 Spot picking
5.1 Overview
DeCyder Differential Analysis Software provides the user with the
functionality to generate a spot pick list which assigns proteins for
picking by Ettan Spot picking robot.
The generation of the pick list in DeCyder Differential Analysis
Software involves the following processes:
• Assigning proteins of interest, which warrant identification on the
analytical gels (i.e. gels analyzed during experiment).
• Spot detection on the preparative gel (gel prepared for picking - see
section 3.3.1).
• Identification of reference markers on the preparative gel.
• Matching analytical and preparative gels.
• Confirming matches between analytical and preparative gels.
• Assign the proteins of interest as spots for picking.
• Confirming spots for picking.
• Exporting a pick list.
There are two principle means of generating a pick list within DeCyder
Differential Analysis Software. The method adopted is dependent on
the size of the experiment and the desired picking criteria. For both
methods, processing the preparative gel in the DIA module is identical,
and is described first.
5.2 Spot detection on the preparative gel
The preparative gel (gel picked from) is prepared and scanned as
described in Ettan DIGE System User Manual.
The preparative gel is normally prepared after the analytical gels have
been investigated. Therefore, it is often necessary to perform spot
detection independently on the preparative gel. This is done as
described in section 3.3.1, employing the single detection algorithm.
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The DIA .XML file for the preparative gel must then be exported
(File:Export Spot Maps) so that it can be loaded into the BVA module for
matching against analytical gels. The .XML file is exported after the
identification of reference markers, if the identification is performed in
the DIA module.
5.3 Identification of reference markers
The reference markers placed on the preparative gels (seen as circles on
the images) act as reference points for Ettan Spot picking robot.
Identification of the reference markers can be done in either the DIA or
the BVA module, the process is identical in both cases.
The reference markers can be detected automatically within the DIA
module during the spot detection process. Once a fluorescently poststained gel image is loaded into the DIA workspace, select
Process:Process Gel Images to open the Process gel images window.
Ensure the Autodetect Picking references check box is selected then click
OK. The reference markers will then be detected automatically during
the spot detection process.
Alternatively the reference markers can be assigned manually. Click on
the Magnify Gel View icon to show the gel images only and select
Edit:Define Picking Reference. Using the circular mouse cursor that
appears in the Image View click on the center of the left reference
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marker of the primary image. Zoom into the area of this reference
marker by holding down the left mouse button to draw a rectangular
area around the marker.
Clicking on the Properties icon and selecting the Image View tab can alter
the size of the target. The value entered in the Default radius of picking
references can be set so that the circle in the Image View is sufficiently
large enough to accurately surround the reference marker. The position
of the circle can be altered by selecting Edit:Edit picking References. Place
the hand shaped cursor that now appears on the circle, then drag the
circle over the center of the reference marker. Move the target so that it
fits exactly around the reference. Magnifying the area of the gel around
the reference marker by selecting the Zoom in icon may make accurate
alignment easier. Select Edit:Edit picking References to exit this mode.
When the first reference has been selected, the same procedure is used
to select the reference on the right hand side of the preparative gel
image.
5.4 Identifying Proteins of Interest
Protein spots are first analyzed then filtered using various spot data
parameters. The spots are filtered on the basis of user defined criteria,
which result in the successfully filtered spot being assigned as a Protein
of Interest (POI). These proteins of interest can be the evaluated then
given a Pick status in order to generate a pick list.
Spots can be identified as a protein of interest using either the DeCyder
Differential Analysis Software DIA or BVA module.
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5.4.1 Identifying proteins for picking using the DIA module
The DIA module is used to identify protein of interest for picking when
performing small-scale experiments utilizing two samples on a single
gel e.g. control-treated samples.
Assignment of POI is performed on all spot maps loaded into the DIA
module. Proteins assigned as protein of interest can then be further
evaluated in the BVA module, prior to assigning a Pick status to those
proteins. The simplest means to assign POI status is to manually choose
spots then select the Protein of Interest check box.
The spots can also be selected using various criteria via the Protein
Filter. Click on the Protein Filter icon to open the Protein Filter dialog
box.
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In the Protein Filter select the Assign Protein of Interest check box and
deselect the Assign Pick Status check box. In this way the proteins that
meet the filter criteria will be given a Protein of Interest status.
All spots detected can be assigned with protein of interest status by
selecting the Pick All check box. Alternatively, spots can be filtered on
specific spot properties.
Protein spots exhibiting a change in expression are commonly
analyzed. Therefore selecting proteins of interest on the basis of volume
ratio is useful for such spots. Spots can be selected for decreases or
increases in order to select spots that have diverged notably from each
other. Alternatively, spots can be selected for decreases and increases
allowing selection of spot volume ratios within a finite limit.
Spots can also be selected on the basis of their physical characteristics
(i.e. area, max volume, max peak height) to ensure that spot selection
only occurs on spots that can potentially be successfully picked,
digested then analyzed by mass spectroscopy. The location of the spots
on the X and Y axes can also be filtered in order that spots near or on
the edge of the gel are not present in the pick list, or to limit the eventual
pick list to high or low molecular weight proteins. The various filters
can be used simultaneously to select spots on multiple features.
This filtering can take place on the whole spot population or only those
confirmed by selecting the Restrict to confirmed spots check box.
After entering the filter criteria, select the Filter button to ascertain the
number of spots that will be picked. If the number of resultant spots is
unsuitable, the stringency of the filter can be adjusted to produce an
optimal number of spots for selection and subsequent picking. Click OK
to accept the spot filter parameters. If spot confirmation was not
performed prior to the picking filter, then the spots selected for picking
can be visually inspected and confirmed.
The protein of interest status of spots can be removed by selecting
Process:Unassign all Protein of Interest.
The DIA file containing the protein of interest selection information
can then be exported as an XML file (File:Export Spot Maps) so that it
can be loaded into the BVA module for matching against a preparative
gel.
Both the preparative gel and analytical gel XML files are loaded into
the BVA module (see section 4.3.1) and matched. Landmarking may be
required to accurately match preparative gels (see section 4.6).
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One of the analytical spot map must be assigned as a template spot map
in the Spot Map Table mode of the BVA module by selecting the
Template check box in the data control panel (see section 4.5).
Spots assigned as protein of interest are denoted by the presence of the
letter “I” in the Function column of the Protein Table and have the
Protein of Interest check box selected.
5.4.2 Identifying proteins of interest using the BVA module
The simplest means to assign POI status is to manually choose spots
then select the Protein of Interest check box when in Spot Map Table
mode.
Alternatively identification of proteins for picking can be based on the
statistical information generated from a BVA workspace containing
several spot maps that have been subjected to statistical analysis within
the BVA module.
Proteins can be selected using various criteria via the protein filter.
Click on the Protein Filter icon to open the Protein Filter dialog box (see
section 4.9 for further information).
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All protein spots detected can be assigned by selecting the Select All
check box. Alternatively, proteins can be filtered for picking based on
the statistical analysis results produced (i.e. based on the statistical
criteria described in section 4.8).
This filtering can take place on the whole spot population or a subset
of the proteins or spot maps. Selecting the Restrict to confirmed proteins
check box ensures that only proteins with a confirm status are filtered.
Alternatively, selecting the Restrict to protein present in check box ensures
that only spots that appear on the designated number of spot maps are
filtered.
Filtering can be limited to spots of a specified volume by selecting the
Volume check box then entering the volume values required.
The location of the spots on the X and Y axes can also be used for
filtering in order that spots near or on the edge of the gel are not
selected or to limit selecting too high or low molecular weight proteins.
The selection criteria for volume and gel region can be based on either
the Master gel or the Pick gel (if present) by selecting the appropriate
spot map in the pull-down menu.
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After entering the filter criteria, select the Filter button to ascertain the
number of spots that will be assigned as proteins of interest. If the
number of resultant spots is unsuitable, the stringency of the filter can
be adjusted to produce an optimal number of spots for picking. Click
OK to accept the spot filter parameters.
Spots assigned as protein of interest are denoted by the presence of the
letter “I” in the Function column of the Protein Table and have the
Protein of Interest check box selected.
The protein of interest status of spots can be removed by selecting
Process:Unassign all Protein of Interest.
The preparative gel XML file, which has to be first generated in the DIA
module (section 5.2) can then be imported into the BVA module. Select
File:Import Spot Map(s), browse to locate the XML file then click Open.
The preparative gel can then be matched (see section 4.6.2).
Landmarking may be required to accurately match preparative gels (see
section 4.6.1).
5.5 Assigning Spots for Picking
As can be seen from the previous section, a BVA workspace can be
created using either the DIA module or the BVA module, to identify a
set of proteins as protein of interest, in a workspace containing a
preparative gel, that has been matched to the analytical spot maps.
The preparative gel should then be assigned as the Pick Spot Map (using
the function check boxes in the Spot Map Table mode).
The spot map Pick status denotes that the co-ordinates for generating
the pick list are based on the designated preparative gel.
It is recommended that the spots assigned as POI are visually inspected
to remove those not suitable for picking and subsequent analysis. This
can be performed in the Protein Table mode of the BVA module. The
Protein Table must initially be organized so that POI proteins are
exclusively displayed in the Protein Table. This is accomplished by
selecting the Protein of Interest (I) only check box in the Protein Table tab
of the Properties dialog window.
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The Function column header in the Protein Table then displays the
proteins with protein of interest status (possessing “I” status in the
function column). The POI assigned proteins can then be reviewed
sequentially to ensure that they are correctly matched and suitable for
picking. Once the spot is deemed suitable for picking select the Pick
check box in the data control panel.
Once the spot is assigned with a pick status it will have a black spot
boundary in the image view and the letter “P” in the Picked column of
the Protein Table. The pick location will then need to be reviewed on
the spot as described below, before reviewing the next spot in the table.
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5.6 Editing Pick Locations
In the BVA module proteins assigned with pick status can be shown
exclusively in the image view when in Protein Table mode. This is
accomplished by opening the Properties dialog window and ensuring
that Spots present in table only check box in the Image View tab is selected
and the Proteins assigned as Pick only check box is selected in the Protein
Table tab.
The Protein Table and the image view will then display only the
proteins that have been assigned with a pick status. Using the Zoom in
icon the individual spots selected for picking can be seen. Each of the
protein spots have a black dot within the spot boundary denoting the
centre of mass of the spot and hence the centre at which a picking head
will pick from the gel. The centre of mass represents the optimal picking
location for a vast majority of spots. However, it may be advantageous
to edit the pick location when two spots are in very close proximity in
order to minimize the possibility of cross contamination. The picking
location can therefore be edited in these cases.
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The picking location must first be displayed in both the image view and
3-D View when in Protein Table mode. The pick location is only
displayed on the spot map assigned as pick (providing it has the picking
references defined). To display the picking locations ensure that the
Picking references and pick locations check box is selected in the Image
View tab of the Properties dialog window.
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The pick locations are displayed as a yellow (by default) transparent
cylinder and a yellow circle in the 3-D View and image view,
respectively when a picked spot on the pick gel is selected. The pick
locations can be edited by zooming in on the selected spot in the image
view, then selecting Edit:Edit Pick Locations. Place the hand shaped
cursor that appears over the centre of the pick location that requires
editing in the image view then drag the pick circle to the desired
location. This can be repeated for pick locations that require editing.
Select Edit:Edit Pick Locations to exit this mode.
To return all pick locations to the initial position of centre of mass select
Edit:Restore Default Pick Locations.
Note: Editing of pick locations can also be performed in the DIA
module. To display only picked spots in the image view select
Picking references and pick locations check box in the Spot
Display tab of the DIA module Properties dialog window.
Editing pick locations is then performed in an identical manner
to the BVA module.
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5.7 Generating a Pick List
Pick lists can be generated for either Ettan Spot Picker or Ettan Spot
Handling Workstation in both the BVA and DIA modules.
Select File:Export Picking List From:Pick Spot Map in the BVA module to
generate a pick list for the preparative gel. A pick list is generated in the
DIA module by selecting File:Export Picking List. The pick list can then
be saved as either a text file or an XML file by selecting the appropriate
file extension to the file name (.txt or .xml). The text and XML file are
used for Ettan Spot Picker or Ettan Spot Handling Workstation,
respectively.
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Batch processor
6 Batch processor
6.1 Overview
The Batch Processor links both the DeCyder Differential Analysis
Software DIA and BVA modules to perform all stages of the 2-D DIGE
analysis process. Once the Batch Processor has been set up, the gels are
processed sequentially without user intervention. The Batch Processor
is, therefore, a means of automation and does not introduce further
processing and analysis to the spot map data. All the concepts for
processing the gel images have been discussed in the DIA and BVA
sections of the manual and are not further elaborated upon in this
section. References to the appropriate sections are made where
necessary.
6.2 Batch list creation
Double click the DeCyder Differential Analysis Software Batch Processor
icon on the desktop to open the Batch Processor.
The creation of the batch list can be broadly sub-divided into two
activities:
• DIA batch list set up. Loading the spot map pairs for co-detection
in the DIA module.
• BVA batch list set up. Stipulating spot map attributes, setting up
the statistical analysis and subsequent pick list in the BVA module.
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6.2.1 Setting up the DIA Batch List
When setting up a DIA batch list each gel is processed sequentially. To
create a batch list select File:New Batch.
In the dialog box that appears browse using the right panel to locate the
folder containing the gel images to be processed. The image files
contained in the selected folder appear in the left panel. Click to
highlight the image designated to be the primary image in the first gel
to be processed (conventionally the primary image is the standard
image in an experiment employing an internal standard). Click the
Primary button to select this image as the primary image.
Repeat the process for the secondary and tertiary image if present using
the appropriate selection buttons, then click OK.
The Gel Image Information and Result Files dialog box automatically
appears.
The CyDye DIGE Fluor dyes for the images can be selected in the
appropriate pull down menus. If the image file names contain the text
“Cy2”, “Cy3” or “Cy5” these fields are automatically assigned.
The DIGE labelling chemistry can be selected (Min is selected as
default).
The spot co-detection process in the Batch Processor generates both a
.DIA and an .XML file for each gel processed. The file name of these
files can be stipulated in the text boxes in the Results File area of the
dialog box. Both file names are automatically given identical names and
are distinguishable by their file extension. It is recommended that the
number “1” is appended to the end of the file name of the first gel.
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Subsequent gels will then be named automatically by incrementing the
number at the end of the file name. If the number “1” is not appended
to the end of the file name then the user is prompted to enter a file name
for each gel. Click OK when all the data is correctly entered.
The Estimated no of spots and BVA Processing dialog box automatically
appears. The estimated number of spots can then be entered; this
estimation is used for all subsequent spot map pairs. See section 3.3.1
for further details.
If the gel is a pick gel, containing picking references which require
autodetection, select the check box for Autodetect pick reference markers.
If BVA inter-gel matching is also required, select the check box Include
in BVA batch list. Click OK.
Note: If the spot map of the preparative gel image contains the text
“pick” or “preparative” in the image file name, the software will
automatically assign the spot map with a Pick status and will
autodetect the references.
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The Exclude Filter dialog box automatically appears.
The Exclude Filter dialog box can either be left empty (in which case
click OK), or a set of known filter parameters can be entered followed
by clicking OK. The filtering values are best pre-determined in the DIA
using one of spot map pairs to be batch processed. See section 3.5.1 for
further details.
The Exclude Filter values stipulated are used on all subsequent gels.
The next dialog box allows a second gel to be entered. All subsequent
sets of images are loaded in an identical manner to the first. When all
the images have been loaded, click Cancel.
The batch information is then entered into the DIA batch list (example
below).
If inclusion of a BVA batch list was selected the user is prompted to set
the BVA workspace.
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Click Yes to set the BVA workspace in order to assign spot map
attributes and set up statistical analysis and generate a pick list if
required.
6.2.2 Setting up the BVA Batch List
The Spot Map Assignment dialog box appears when setting up the BVA
workspace.
As with the Spot Map Table mode in the BVA module, attributes (i.e.
spot map function, group, conditions, sample ID and spot map
comments) can be assigned to individual spot maps. Attribute
assignment is performed in the above dialog box.
Attributes are assigned sequentially to each spot map by selecting the
appropriate spot map function or entering the desired experimental
design criteria. Click OK once all the attributes are assigned to the first
spot map. The Spot Map Assignment dialog box for the second spot
map will automatically appear. The spot map attributes for all spot
maps can therefore be assigned in this iterative process.
All attributes are assigned as in the Spot Map Table mode (section 4.5.1
and 4.5.2). In the Batch Processor, groups are added by selecting the
Add button in the Spot Map Assignment dialog box.
Note: As with the BVA module, if no master image is stipulated the
internal standard spot map, (if present) with greatest number of
detected spots is automatically assigned as the master image
when running the batch list.
Once all spot maps attributes have been defined the Protein Statistics
dialog box automatically appears. This allows the user to perform
statistical analysis on the experiment. The appropriate statistical
analysis can be set up as described in section 4.8. Click OK to confirm
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the statistical analysis entered. Alternatively, if no statistical analysis is
required click Cancel.
The Protein Filter dialog box then appears automatically. The filter
allows either the highlighting of interesting proteins for further
investigation or the direct identification of proteins for picking and
subsequent generation of a pick list, if a pick gel is present in the batch
list (see Chapter 5).
Note: It is recommended that the Protein Filter is used to highlight
interesting proteins for further investigation. These proteins can
subsequently be confirmed by the user in the BVA module prior
to generating a pick list (see section 5.4.2).
6.3 Editing the batch list
6.3.1 DIA batch list
To edit the DIA batch list select View:DIA Batch List to ensure the DIA
batch list is displayed.
Adding
To add further spot map pairs to a current DIA batch list, select the next
empty row in the batch list and select File:Add DIA batch item. Extra gels
are added as described in section 6.3.2.
Removing
To remove an item, select the item from the DIA batch list, select
File:Remove DIA batch item then confirm that the item is to be deleted.
The item will then be automatically removed.
Editing
If an incorrect image is selected while loading the DIA batch list,
continue loading the rest of the images then amend the desired image at
the end.
To amend the batch list, select the desired item from the DIA batch list,
and then select Edit:Edit Item.
6.3.2 BVA batch list
To edit the BVA batch list select View:BVA Batch List to ensure the BVA
batch list is displayed.
Adding
To add a spot map(s) to the BVA batch list, select File:Add Spot Map(s).
Browse to locate and select the XML files corresponding to the spot
map(s) processed in a previous DIA analysis. Click Open. The spot
map(s) will automatically be added to the batch list.
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Removing
To remove an item, select the item from the BVA batch list, select
File:Remove BVA batch item then confirm that the item is to be deleted.
Editing
To amend the spot map attributes of an item in the BVA batch list, select
the desired item from the BVA batch list, then select Edit:Edit Item.
6.4 Saving Data
The batch list, containing all the information entered into the Batch
Processor, can be saved by selecting File:Save As (files are saved as text
files). The batch list text files can then be re-opened later by selecting
File:Open.
The folder that the DIA and XML files generated when processing the
DIA batch list can be selected by clicking the Browse... buttons adjacent
to the respective directory text box.
Similarly, if the BVA batch list is also performed, the names and
destinations for saving the subsequent BVA file and pick list (if
requested) can be stipulated by selecting the Set... buttons adjacent to
the respective file name text box.
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6.5 Running the batch processor
To run the Batch Processor select Process:Run batch. If the default BVA
filename has not been changed the user will be given the option to
change this.
The Run batch dialog box, which now appears, gives the user the option
to begin processing immediately or introduce a delay period. The batch
processor must be left open during the delay period. To begin the
countdown to processing or to start processing, click OK.
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7 XML Toolbox
7.1 Overview
Large amounts of data are generated when a workspace is created and
analyzed in DeCyder Differential Analysis Software. It is useful to be
able to save this data in a format that can be efficiently stored and
queried. Data is exported from the different DeCyder Differential
Analysis Software modules using a common XML format called
DeCyder Differential Analysis Software XML. XML is a structured
universal tagged language with tags that can be custom designed,
making it easy to access data from DeCyder Differential Analysis
Software analysis workspaces for other applications. The XML format
is partly used to transfer data between the different modules of the
DeCyder Differential Analysis Software but it is also used to make data
available for post processing by other software packages such as
database building packages.
The DeCyder Differential Analysis Software XML Toolbox is a toolbox
shell housing different tools for extraction of the DeCyder Differential
Analysis Software data from the different XML files produced within
DeCyder Differential Analysis Software. This enables users to create
tools to convert their data into text files or html files (potentially other
data formats can be supported for conversion). Two basic tools are
supplied to create Tabbed text files and Web tables.
7.2 Opening the XML Toolbox module
Click on the Toolbox icon to open the XML Toolbox.
The XML Toolbox can extract data in two formats: Tabbed text files
and Web tables.
Tabbed text files
The Tab Separated Tables tool is used to export DeCyder Differential
Analysis Software XML data in a tabbed text format for further data
processing in other software that can import this data (e.g. Microsoft
Excel or SpotFire™). The data in the resulting output table is
configured such that the top row consists of an identifying line of text,
containing the name of the source XML file and the type of data
extracted from the file. The second row in the output table is a tabseparated list of column headers, and the following rows consist of the
actual tab separated raw data.
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To utilize the Tab Separated Tables tool click on the Tab Separated Tables
button.
Web tables
The Web Tables tool is an example of how the DeCyder Differential
Analysis Software XML format in combination with XSL and other
web technology can be used to customize reports in HTML format. The
Web Tables tool can thus be used as a template for user designed report
tools.
To utilize the Web Tables tool click on the Web Tables button.
The interface for both tools is similar.
7.3 Extracting data
Loading the XML files
XML files can be loaded at the top of the tool page, by either browsing
for a file then clicking Open or by typing the file pathway directly into
the list box then pressing enter on the keyboard. The tool verifies
whether the selected file is a valid XML file exported from DeCyder
Differential Analysis Software-DIA or DeCyder Differential Analysis
Software-BVA. The user is alerted if the selected file is not valid.
Type of proteins in the output table
Once the DeCyder Differential Analysis Software XML file has been
opened, select the types of proteins to be included in the output table.
For XML files exported from BVA, four selections are available, Picked
proteins, Proteins of interest, All matched proteins, and All proteins. For
XML files exported from DIA, only the Picked proteins and All proteins
selections are available.
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Category of data in table columns
The data is categorized into four groups:
1
Experiment data. The table contains general experimental set up
parameters valid for the current DIA or BVA experiment.
2
Image data. The data in this table contains the different parameters
that are unique to each spot in the individual gel images contained
in the selected DIA or BVA experiment.
3
Gel data. The data displayed in this table is unique for each detected
spot pair within each set of gel images that originate from the same
gel.
4
Protein data. All protein data that is unique to a set of matched spots
across the different gels in the current BVA experiment is displayed
in this table.
The interfaces diverge when selecting categories of data:
• Web tables. Each of the four lists of data items represents a type of
result table. Thus if at least one item in each list is selected, the
resulting output contains four different result tables. Multiple
selections are possible in all lists. The content of the four data lists
also changes, depending on whether an XML file is exported from
the DIA or BVA module.
• Tabbed text files. Select a category of DeCyder Differential
Analysis Software data to include in the result output from the pull
down menu. Depending on the selection of category and type of
XML file selected, a number of items are displayed in the list box
below. For the Experimental data and Protein data categories,
multiple selections are possible in the list of data items, but for the
Image data and Gel data categories, only a single selection is
possible.
Extracting Data
Once the data output has been specified, the data can be extracted by
clicking the Extract data button.
The data extraction process can take from a few seconds to several
minutes depending on the size of the selected file, the amount of data
extracted and the performance of the computer. Whilst the data is being
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extracted, a progress window displays the size of the XML file. Once
extracted, the Result Table is displayed in a separate browser window.
A floating Save As button is present in the upper right hand corner of
the output window. This button can be used to save the extracted
output directly to a designated file for use by other applications.
7.4 Tag definitions
7.4.1
BVA parameters
Experimental data
Condition 1. Numerical condition 1 assigned to the spot map group.
Condition 2. Numerical condition 2 assigned to the spot map group.
DIA experiment. File name of the corresponding DIA workspace.
Dye chemistry. Indicates whether minimal or saturation labelling was
used.
Dye label. Indicates the fluor used to label the protein.
Group Description
Group. Name of the experimental group that the spot map has been
assigned to.
Image name. Gel image filename.
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No. of included spots. Number of spots included in the data set.
No. of matched spots. Number of spots matched to the master image.
Sample ID. Numerical identifier assigned to spot maps for paired
statistical analysis.
Spot map comment. User defined comment on selected spot map.
Spot map function. Spot map function in DeCyder Differential Analysis
Software BVA. M, A, T and P represent master, analysis, template and
pick respectively.
Image data
Volume. Sum of the detected pixel values above background within a
spot boundary.
Peak height. Pixel value at the X,Y position of the spot. This represents
the highest pixel value within the spot boundary.
Normal volume. Normalized volume obtained by dividing the calculated
volume for each included spot by the center of volume of the
corresponding spot map.
Gel data
Volume ratio. Ratio of the normalized volumes of a pair of spots from a
spot map pair. A value of 2.0 represents a two-fold increase while -2.0
represents a two-fold decrease, whilst a value of 1.00 represents an
unchanged spot.
Spot comment. User defined comment on selected spot.
Picked status. Indicates whether the protein has been assigned for
picking.
X-pos. Position of a spot peak along the horizontal axis of the spot map.
Y-pos. Position of a spot peak along the vertical axis of the spot map.
Protein ID. Unique protein identifier that can be used to search databases
providing it is in the correct format for the specific database.
Area. Area as expressed in pixels within a spot boundary.
Match confidence. Indicates the type of match achieved i.e. Unmatched,
Auto Level1, Auto Level2, Manual or Added (to Master Spot Map).
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Match status. Indicates whether match has been confirmed.
Spot number. DIA spot number as given by the spot detection algorithm.
Protein data
Picked status. Indicates whether the protein has been assigned for
picking.
Protein ID. Unique protein identifier that can be used to search databases
providing it is in the correct format for the specific database.
Protein AC. The protein accession number as designated by the selected
database.
Protein Name. Protein name given by selected database.
Protein comment. Note typed in the protein comment box
Protein function. Proteins assigned Protein of Interest are marked with an
“I” in this column.
pI. Isoelectric point of the protein.
pI Status. Indicates whether displayed isoelectric point was entered for
reference purpose of the algorithm, calculated from the selected
algorithm or manually edited (designated Template, Calculated and
Manual respectively).
Mw. Molecular weight of protein.
Mw Status. Indicates whether displayed molecular weight was entered
for reference purpose of the algorithm, calculated from the selected
algorithm or manually edited (designated Template, Calculated and
Manual respectively).
T-test value. Student’s T-test p value.
Average ratio. Average ratio between the groups selected for protein
analysis.
ANOVA1. One-way ANOVA (ANalysis Of VAriation) p value.
ANOVA2 condition1. Two-way ANOVA for condition 1 p value for
condition 1.
ANOVA2 condition2. Two-way ANOVA for condition 2 p value for
condition 2.
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ANOVA2 interaction. Two-way ANOVA interaction p value for the
interaction between condition 1 and condition 2.
Paired T-test. Paired Student's T-test p value.
Paired average ratio. Paired average ratio between the groups selected for
protein analysis.
RM ANOVA1 (Repeated Measures ANOVA). Paired One-Way ANOVA p
value.
RM ANOVA2-condition1. Paired Two-Way ANOVA p value for
condition 1.
RM ANOVA2-condition2. Paired Two-Way ANOVA p value for
condition 2.
RM ANOVA2-interaction. Paired Two-Way ANOVA p value for the
interaction between conditions 1 and condition 2.
7.4.2
DIA parameters
Experimental data
Primary Image. Refers to the left hand gel image present in the image
view.
Secondary Image. Refers to the right hand gel image present in the image
view.
DIA experiment. Gel name/designation.
No. of included spots. Number of spots designated as included for
subsequent analysis.
Dye chemistry. Indicates whether minimal or saturation labelling was
used.
Estimated no. of spots. User defined approximation of the number of
spots present in spot map image entered prior to spot detection.
No. of similar spots. Number of spots within the threshold mode set.
No. of increased spots. Number of spots showing an increase in
abundance, above the threshold mode set from the secondary image
compared to the primary image.
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No. of decreased spots. Number of spots showing a decrease in
abundance, above the threshold mode set from the secondary image
compared to the primary image.
Threshold mode. Value above or below which spots are classed as being
differentially expressed.
2SD value. 2 standard deviation of the spot ratio distribution, 95% of
the spots lie within this ratio for normally distributed data.
Image data
Volume. Sum of the detected pixel values above background within a
spot boundary.
Peak height. Pixel value at the X,Y position of the spot. This represents
the highest pixel value within the spot boundary.
Spot slope. Gradient associated with the 3 dimensional attributes of a
spot map pair.
Gel data
Volume ratio. Ratio of the normalized volumes of a pair of spots from a
spot map pair. A value of 2.0 represents a two-fold increase while -2.0
represents a two-fold decrease, whilst a value of 1.00 represents an
unchanged spot.
Spot comment. User defined comment on selected spot.
Picked. Indicates whether the protein has been assigned for picking.
Coordinates. Values giving the position of the center of mass of a spot on
the spot map.
Protein ID. Spot identifier, which after confirmation assignment will
appear with the relevant spot in the table view.
Area. Area as expressed in pixels within a spot boundary.
Peak height ratio. Ratio of the normalized peak heights of a pair of spots
from a spot map pair. A value of 2.0 represents a two-fold increase
while -2.0 represents a two-fold decrease, whilst a value of 1.00
represents an unchanged spot.
Spot radius. Refers to the radius that the spot would have if it was
circular and had the same area.
Excluded. Indicates whether spot has been excluded.
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Confirmed. Indicates whether a spot has been manually confirmed by the
user.
Confirm Status. Indicates whether the user has confirmed protein as real.
Auto Confirm Assignment. Refers to the spot status after application of the
exclude filter. Spots are either designated as excluded or included.
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8 LWS Integration
8.1 Overview
The XML Toolbox has been designed to enable the smooth transfer of
files containing pick lists from DeCyder Differential Analysis Software
to Ettan LWS software (product number 18-1164-06), and the transfer
of protein identification results from Ettan LWS software, after MALDI
mass spectrometry, back into DeCyder Differential Analysis Software.
Fig 8-1. Ettan LWS - DeCyder XML Tools in Ettan DIGE system workflow.
To enable full tracking of the CyDye DIGE Fluor dye treated samples
some extra information has to be added in the Sample Definition
component of the Ettan LWS software.
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The XML toolbox contains two tools to perform data conversion:
• LWS Pick List converts pick lists from a DeCyder Differential
Analysis Software workspace to a format that Ettan LWS can
import.
• Protein Identification converts results from Ettan LWS in .xml
format to a format that the BVA module can import.
This section of the manual aims to:
• describe the deviation from the normal 2D-MS workflow as
described in Ettan 2D-MS and LWS Software Laboratory Guide for
samples generated within Ettan DIGE system
• describe how the LWS Pick List tool is used to convert exported
workspace data from the DeCyder Differential Analysis Software
evaluation into a pick list format Ettan LWS 1.0 can read
• describe how the Protein Identification tool is used to import the
protein identification results from Ettan LWS data back into
DeCyder BVA
8.2 Ettan LWS with samples generated within
Ettan DIGE system
There are two alternative ways to enter a sample generated within Ettan
DIGE system, into Ettan LWS workflow:
•
as a protein tube or;
• as a gel with a pick list and an image file.
The two alternatives offer different levels of sample tracking. For a
protein tube, tracking is available all the way through Ettan LWS
workflow. For a gel, the tracking starts at spot handling. The
workflows are also different, see Fig. 8-2.
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Fig 8-2. Overview of the workflow for a sample generated within Ettan DIGE system entered as a tube or as a gel.
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8.3 Enter a protein tube generated within Ettan DIGE system,
into Ettan LWS
The workflow for the samples should be set up the normal way in Ettan
LWS. The only differences are within sample attributes template and
sample definition.
A user defined sample attribute template must be set up to match the
experimental design within Ettan DIGE system. By creating a DIGE
sample template in Sample Attribute Templates it will be possible to
later search for the information and to produce Ad hoc reports. For
information on Ad hoc reports, please consult Ettan 2D-MS and LWS
software Appendices.
1
In the Management Workbench:Sample Template Editor, define a
sample type for a sample generated within Ettan DIGE system, by
adding user defined attributes. An example is shown in Fig. 8-3.
Fig 8-3. Example of attributes for a sample type generated within Ettan DIGE system.
The samples need to be defined in Scierra™ web.
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2
In Scierra web select project name and Define Samples.
3
Scan or type sample ID, choose Sample Type (Protein), Sample
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Template (the user defined template created in step 1) and Container
(tube) and enter values of the sample attributes. An example is
shown in Fig. 8-4.
Note: The extra information (Sample attribute values) helps the user to
keep track of which samples are in each gel.
Fig 8-4. Example of Sample Definition and entered values for the attributes when
entering a sample generated within Ettan DIGE system as a protein tube.
Methods
Normally the standard methods included in Ettan LWS software and in
Ettan MALDI-ToF Pro can be used. If non-standard methods are to be
used these methods have to be added:
• In Management Workbench:System:Method editor, add staining
method.
• In Scierra LWS select Spot Handling from the Workflow Graph. In
Tools:Spot Handling Method Management:Method Overview, add Spot
Handling methods.
• In Ettan MALDI Control:Methods, add MALDI methods.
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8.4 Enter a gel, prepared for spot picking within
Ettan DIGE system, into Ettan LWS
A preparative gel or an analytical gel with CyDye DIGE Fluor dye
labelled proteins can be used as a picking gel. For details of the methods
for the different routes to picking, refer to chapter 5.
The workflow for the gel should be set up the normal way in Ettan
LWS. The only differences are within sample attributes template and
sample definition.
1
Set up a sample attribute template as in 8.3, step 1.
The samples need to be defined in Scierra web.
2
In Scierra web select project name and Define Samples.
3
Scan or type sample ID, choose Sample Type (Protein), Sample
Template (the user defined template created in step 1) and Container
(Gel 2D) and enter values of the sample attributes.
An example is shown in Fig. 8-5. This extra information helps the
user to keep track of which samples are in each gel.
4
Select Glass backing, fill in backing thickness 3.2.
5
Browse for the image file and the converted evaluation file (converted
according to section 8.5) to be used.
Note: Image files to be included in the sample definition must have a
file name of less than 45 characters, otherwise a work request for
that sample cannot be generated and an error message will be
shown.
Note: If samples generated within Ettan DIGE system are entered as a
gel it will only be possible to track the samples in the spot
handling and MALDI mass spectrometry steps.
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Fig 8-5. Example of Sample Definition and entered values for the attributes when
entering sample generated within Ettan DIGE system as a gel
Methods
Normally the standard methods included in Ettan LWS software and in
Ettan MALDI-ToF Pro can be used. If non-standard methods are to be
used these methods have to be added:
• In Management Workbench:System:Method editor, add staining
method.
• In Scierra LWS select Spot Handling from the Workflow Graph. In
Tools:Spot Handling Method Management:Method Overview, add Spot
Handling methods.
• In Ettan MALDI Control:Methods, add MALDI methods.
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8.5 XML toolbox - LWS Pick List
The LWS Pick List tool is used to transform DeCyder Differential
Analysis Software data into an Ettan LWS 1.0 format. The tool takes a
DeCyder .xml file exported from BVA or DIA, extracts the spots in the
file that are selected for picking in DeCyder Differential Analysis
Software and creates a new .xml file for import to Ettan LWS software.
8.5.1 Preparations before XML conversion
1 If possible, base pick lists on information in a BVA workspace
rather than a DIA workspace. It is a lot easier to find relevant
protein spots to pick in the BVA module as it includes an overview
of all experimental conditions.
In BVA workspace: use a workspace including spot maps from a
picking gel that contains real pick references. A spot map from the
gel to be used for picking needs to be assigned as a pick spot map
before exporting the XML workspace file.
In DIA workspace: use a workspace based on gel images from a
picking gel that contains two real pick reference markers. If a DIA
workspace is used to generate a pick list for LWS it will not be
possible to import protein data for that pick list into the BVA
module using the protein identification tool.
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2
Assign the pick references in DeCyder Differential Analysis
Software.
3
Export the workspace from BVA by selecting File:Export Workspace
or in DIA by selecting File:Export Spot Map. The file must be saved
to a location accessible by the XML Toolbox.
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8.5.2
Using the LWS Pick List tool
1
Either browse for a file or type the path of the file into the Load XML
file generated from DIA or BVA field at the top of the tool page.
2
Select either View pick list in separate window before saving or Save pick
list without viewing result.
3
Click Create Pick List to start the conversion procedure. At the end
of the conversion procedure a Save As dialog is displayed. Enter the
name of the file where the pick list is saved. Type the extension .xml
to the file name. Save the file in a location accessible by Scierra
LWS.
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8.5.3 After XML conversion
In Ettan LWS, the converted pick list file (from section 8.5.2) will now
be used as the evaluation file together with the gel image file in sample
definition for the 2D-gel (see section 8.4).
Note: If you have entered the sample generated within Ettan DIGE
system as a tube (see section 8.3), follow the normal workflow
in Ettan LWS using the converted pick list file as the evaluation
file in Image Analysis on the Image Results tab.
After entering the pick lists in Ettan LWS do not modify the original
DeCyder BVA workspace. If changes are made to the BVA workspace
between exporting the pick list data and importing the annotated Ettan
protein identification result, it cannot be guaranteed that the imported
protein IDs are still consistent with the information in the BVA
workspace. If changes are required, it is advisable to make these
changes on a copy of the original.
8.6 XML Toolbox - Protein Identification
The Protein Identification tool is used to transform Ettan LWS data
back into DeCyder Differential Analysis Software format. It is then
possible to compare the original gel images in DeCyder BVA with the
protein identification results from the MALDI mass spectrometry
analysis in Ettan LWS.
The report “Detected proteins in gel” from Scierra LWS (saved in .xml
format) is used as the input file for the Protein Identification tool. This
file is converted to an .xml file that can be imported into the BVA
module.
Data that can be imported from Ettan LWS to DeCyder BVA are:
• Protein ID. If multiple protein candidates are included in the LWS
report for the different picked spots, the protein Id's of the different
candidates are all appended in a single string, separated by semicolons (;).
• Protein Name. If multiple protein candidates are included in the
LWS report for the different picked spots, the protein names of the
different candidates are all appended in a single string, separated
by semi-colons (;).
• Molecular Weight (MW). The MW value of the first protein
candidate in the list is added to Ettan Protein Identification data.
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• Isoelectric Point (PI). The pI value of the first protein candidate in
the list is added to Ettan Protein Identification data.
8.6.1 Preparations in Ettan LWS before XML conversion
After MALDI mass spectrometry is completed the “Detected proteins
in gel” report can be generated.
1
In Scierra Web select Tools:Reports and generate the standard report
“Detected proteins in gel” as described in Ettan 2D-MS and LWS
Software Laboratory Guide.
Note:
Select how much information to import into DeCyder BVA
by altering the maximum rank and by including subrankings or not.
The report will appear on screen.
Fig 8-6. Save the report “Detected proteins in gel” as XML.
2
Click XML in the middle of the dialog to save the file. Save it to a
location accessible by XML Toolbox.
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8.6.2
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Using the Protein Identification tool
1
Either browse for a file or type the path of the XML file containing
the “Detected Proteins in Gel” LWS standard report into the Load
XML file containing “Detected Proteins in Gel” LWS standard report field
in the middle of the tool page.
2
Select either View template spot map in separate window before saving
or Save template spot map without viewing result.
3
Click Create Ettan Protein Identification to start the conversion
procedure. At the end of the conversion procedure a Save As dialog
is displayed. Enter the name of the file where the Ettan protein
identification file is to be saved. Save the file in a location accessible
by DeCyder Differential Analysis Software.
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LWS Integration
8.7 Importing protein data templates into the BVA module
1
Open the original BVA workspace and select File:Import:Protein
Data.
Note:
If the BVA module suspects that the file for importing
originates from a different picking context than the current
workspace a warning is displayed. It is, however, still
possible to import the information if desired, but it is not
recommended.
8.8 Database queries in DeCyder BVA
In DeCyder BVA it is possible to make online database queries based on
the protein ID of the individual proteins. The protein ID:s generated by
Ettan MALDI-ToF Pro follow the notation used in the NCBI nr (nonredundant) database.
To be able to use the database query function of DeCyder BVA based
on the protein ID:s from Ettan MALDI-ToF Pro, a new query definition
that matches the NCBI nr format must be added in the database
property page.
When adding a new database query definition enter information in the
two fields in the dialog Edit web database label and address:
1
In the Label field type NCBI nr: Protein search based on ID or
something similar.
2
In the Address field type the following search string:
http://www.ncbi.nlm.nih.gov/entrez/eutils/
efetch.fcgi?rettype=gp&retmode=html&db=protein&id=#BVA_PROTID#
3
Click OK.
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Fig 8-7. Information to enter in the dialog Edit web database label and address
when adding the new database query definition.
For further information on using the database query function and
adding new database query definitions see section 4.12 or DeCyder
Differential Analysis online help.
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Fig 8-8. Example of results in DeCyder BVA after import and database query.
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Tutorials Introduction
9 Tutorials Introduction
9.1 DeCyder Differential Analysis Software structure
The DeCyder Differential Analysis Software suite is made up of four
user interfaces:
DIA (Differential In-gel Analysis) – Protein spot detection and
quantitation on images, from the same gel.
BVA (Biological Variation Analysis) – Matches multiple images from
different gels to provide statistical data on protein expression levels.
Batch Processor – Fully automated image detection and matching of
multiple gels without user interaction.
XML Toolbox – Comprises tools for the extraction of data from the
different XML files produced in DeCyder Differential Analysis
Software.
DIA
DeCyder DIA processes a set of gel images (each saved as either 16-bit
TIFF or customized .GEL files) from a single gel. Each image in the pair
is generated from samples labelled with different fluors. Images must be
processed in the DIA interface prior to data analysis in BVA. The DIA
algorithms detect spots on a combined image derived from merging
individual images from an in-gel set of images. This co-detection
ensures that all spots are represented in all images. DIA then
quantitates spot protein abundance for each image and expresses these
values as a ratio thereby indicating changes in expression levels by
direct comparison of corresponding spots.
The data can be saved as a .DIA file from which spot pick lists can be
exported as a text file. In addition data can be exported in an XML
format (which can be queried using the XML toolbox) for multi-gel
analysis in DeCyder BVA or copied directly from DIA and pasted into
applications such as Microsoft Word and Excel.
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Fig 9-1. Schematic representation of DeCyder Differential Analysis Software DIA
workflow.
BVA
DeCyder Differential Analysis Software BVA utilizes the DIA exported
.XML files together with the original gel images to match protein spots
on different gels. Data is then subjected to statistical analyses to
accurately assess protein expression changes occurring in biological
replicates comparing different conditions/treatments. This multi gel
approach, which allows analysis of replicates, provides greater
statistical validity to findings.
The final data can be saved as a .BVA file, from which spot pick lists
and .XML files can be exported. In addition data can be copied directly
from BVA and pasted into applications such as Microsoft Word and
Excel.
Fig 9-2. Schematic representation of DeCyder Differential Analysis Software BVA
workflow.
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Tutorials Introduction
Batch processor
The Batch Processor integrates both the DIA and BVA modules,
enabling fully automated processing of multiple gels with minimal user
intervention. The Batch Processor can be configured to analyze several
gels in the DIA exclusively. Alternatively multiple gels can be processed
through both interfaces to produce a final .BVA file.
XML Toolbox
The XML Toolbox enables the extraction of user specific data from the
XML files generated in either the DIA or BVA modules. This data can
be saved in either text or html format enabling users to access data in
the DeCyder Differential Analysis Software workspaces for other
applications. The XML Toolbox also provides an interface for linking
Ettan DIGE system data with Ettan Laboratory Workflow System.
9.2 Scope of tutorials
The following tutorials are aimed at introducing the functionality of
DeCyder Differential Analysis Software within the context of an actual
experiment. The tutorials have been designed to be a step by step guide
utilizing gel images and DeCyder Differential Analysis Software files
which are co-installed with the tutorials. The four tutorials cover
different aspects of the software suite. They are all self contained and
can be undertaken independently.
To assist the user each tutorial includes a completed version of the
DeCyder Differential Analysis Software file, which the tutorial is
designed to generate. These files all include the word finished in their
names.
Tutorial I
The DIA is used exclusively to demonstrate a small-scale experiment to
assess protein changes in samples that are limited. See chapter 10.
Tutorial II
The second tutorial encompasses a similar experiment to that found in
tutorial 1. However, this tutorial utilizes gels that have already
undergone spot detection in the DIA and have been exported in an
XML format. The processes involved in configuring the BVA, without
the Batch Processor, are demonstrated. See chapter 11.
Tutorial III
The processes required to analyze a “picking” gel for subsequent spot
picking, digestion and mass spectrometry analysis are demonstrated.
See chapter 12.
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Tutorial IV
The tutorial employs all three software interfaces to ascertain protein
expression changes in an experiment containing replicate gels in order
to generate a pick list for Ettan Spot Picker. The entire workflow is coordinated via the Batch Processor, thereby providing a fully automated
process. See chapter 13.
The tutorials described above introduce the concepts and functionality
of DeCyder Differential Analysis Software. It is therefore recommended
that these tutorials are performed first to gain a preliminary
understanding of the software. However it is recommended that
various elements of the tutorials are used when analyzing an
experiment incorporating an internal standard. A workflow describing
the various steps involved in an entire experiment are described in
detail in appendix A.
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10 Tutorial I - Using DIA module for preliminary
investigation of protein changes
10.1 Objective
This tutorial describes how to find protein abundance differences above
experimental variation between a control and a treated protein lysate
of bacterial cultures using DeCyder Differential Analysis Software DIA
(Differential In-gel Analysis). The procedure in this tutorial can be
applied to any two protein mixtures/lysates.
This experimental design can be used to rapidly identify proteins that
show a substantial change in abundance from control to treated sample
and is often used at the preliminary stages of an experiment.
The method is used to gain early information for the samples in
question, and to check that the experimental procedures are
appropriate for the new samples.
The major drawbacks of this approach are that only one control sample
and one treated sample are studied. There are no gel replicates or
biological replicates and therefore the biological relevance and
statistical validity of an altered protein is impaired. This experimental
design only addresses differences above system variation (see section
1.3 for different sources of variability). Since there are no biological
replicates the inherent biological variation within populations is not
considered. This is also true if the biological control and treated sample
are pools of control and treated populations, as this approach is
looking at averages but not variation associated with the different
populations.
10.2 Overview
1
An experimental design using two gels is set up, “control-control”
gel and “control-treated” gel.
2
The control-control gel is first analyzed in the DIA module. From
this, an indication of overall system variation is determined by
elucidating the fold change 95% of spots lie within, e.g. 95% of
spots on control-control gel are within 1.5 fold.
3
The control-treated gel is subsequently analyzed in the DIA
module. As the control-control gel gave a result that 95% are
within X fold (e.g. 1.5 fold) then any spots with a fold change
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greater than X fold (e.g. 1.5 fold) has a 95% confidence that it is a
difference between the control and treated sample above
experimental variation.
4
The spots greater than X fold on the control-treated gel are looked
at in detail. These can be assigned with a protein of interest status
and can be confirmed in preparation for spot picking. Spot picking
from a preparative gel is covered in tutorial III.
10.3 Experimental design
Two gels were loaded with bacterial lysates as indicated in the table
below.
Gel
1
2
Cy3
Control
Control
Cy5
Control
Treated
The control-control gel (gel 1) is analyzed to determine the level of
experimental variation. The protein abundance variation calculated in
DIA indicates the degree of similarity between the protein spots from
two images from the same gel. Ideally there should be no difference
between the proteins from the two control-control gel images (because
the same sample is being run on the same gel). However due to different
experimental factors (for example, pipetting variation) there is some
intrinsic variability.
The data analysis is performed in three parts using the DeCyder
Differential Analysis Software DIA module:
• Analysis of control-control gel
• Analysis of control-treated gel
• Creating a spot pick list
10.4 Analysis of control-control gel
10.4.1 Selecting gel images
1 Double click the DeCyder Differential Analysis Software DIA icon
on your desktop to open the DIA module.
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2
Select File:Create Workspace.
3
Select Double Detection, then click OK.
4
Browse to locate the folder entitled Tutorial I in the Tutorial data files
folder. Double click on the image Gel 01 Control Cy3 to select the
primary gel image.
5
When the Load Secondary Gel Image window appears, double click
on the image Gel 01 Control Cy5.
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10.4.2 Spot detection
1 After the images have been loaded select Process:Process Gel images.
2
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In the process gel images dialog box which now appears enter the
value 2500 for the estimated number of spots (see help file for an
explanation of this value). Click OK to begin spot detection on the
images.
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Before going any further, it is important to become acquainted with the
main tool bar.
The user interface is divided into four main windows, the Image View,
the Histogram View, the 3-D View and the Table View. In addition the
Data Control Panel at the bottom of the screen allows user defined
attributes to the spot selected in the four views.
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The four views are all linked. Clicking on a spot on the Image View
highlights the spot in magenta in the Histogram View. The spot is also
represented three dimensionally and is highlighted in gray in the Table
View.
3
To display the entire gel image, click on the Fit to window icon.
4
After detection it is important to save the workspace. Select
File:Save Workspace As, and place an appropriate name in the dialog
box, which now appears.
The detected spots are of three types: increased, decreased and
similar (colored blue, red and green, respectively, in the Histogram
View)
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5
If, after detection (the hourglass turns back to a cursor) the Table
View remains empty click on the Properties icon to bring up the
properties dialog box. By default this will open on the Workspace
tab.
6
Change to the table options by clicking on the Table View tab.
7
By selecting and deselecting the check boxes the various categories
of spots can be selectively displayed. Deselecting all the check boxes
results in the Table View being blank. Similarly, spots can be
selectively displayed in the image view using the Spot Display tab.
The Table View tab works on OR logic, i.e a spot only needs to
conform to one criterion to be shown in the table.
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Selecting different protein spots on the images reveals that some of the
spots that have been detected are in fact either dust particles or artifacts
from the gel. To remove these artifacts an Exclude filter must be used
on the images.
10.4.3 Assigning an area of interest
Due to gel heterogeneity at the edges of the images, there are artifacts
that need to be removed. These can be removed by setting an area of
interest. This function only works with the filter. If an area of interest
was set before detection all the spots on the image will still be detected
(even those outside the area of interest).
1
Click on the Fit to window icon on the tool bar to fit the images to
the Image View.
2
Click on the Image view icon on the tool bar to have a full screen
view of the gel images.
3
Next, click on the Properties icon to bring up the properties window.
4
Select the Spot Display tab and deselect Similar, Increased and
Decreased, so that the check boxes are identical to those shown in
the figure below. Click OK.
5
To set an area of interest select Edit:Define area of interest. Using the
rectangular target pointer which now appears drag the mouse to
draw a rectangle around the gel, ensuring that edge artifacts are
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removed. It does not matter if the Reference Markers are inside or
outside the Area of Interest.
10.4.4 Gel artifact removal
A 3-D View clearly shows if a detected spot is a gel artifact rather than
a protein spot due to the very steep sides and pointed top of an artifact
compared to the smooth curve of a protein spot.
In order to exclude these artifacts from subsequent analysis a set of
Exclude filter parameters must be generated.
1
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In the Table View click on the column header Max slope until the
spot with the highest max slope is present at the top of the table.
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2
Scroll down the table from one spot to another until the next spot
in the table is an actual protein spot and not a dust particle or gel
artifact. Make a note of the Max slope value of the artifact
immediately before the real protein spot. This value will be used for
the Slope category in the Exclude filter.
3
Repeat this procedure for Area, Max Peak Height and Max Volume, but
in each case ordering the table so that the smallest value is at the
top of the table.
4
When all the values have been found, select Process:Exclude Filter to
display the Exclude Filter dialog and enter the found values.
Click OK.
An example of a non-stringent set of filter parameters that can be used
to run a light filter is as follows:
Slope
Area
Peak Height
Volume
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100
10000
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10.4.5 Ascertaining the 2 S.D. (standard deviation) threshold value
The 2 S.D. value calculated for the control–control gel is the fold ratio
that encompasses 95% of the spots and can be used as a guide to the
level of experimental variation. This value should be noted and can be
set as the threshold mode when analyzing the control-treated gel.
The protein differences being observed on the control-treated gel will
be above the experimental variation.
In the Histogram View is a Histogram Selections toolbar with two drop
down menus and two information boxes. Make a note of the value in
the information box entitled 2 S.D.
10.5 Analysis of control-treated gel
10.5.1 Selecting gel images
1 Select File:Create workspace (there will be an automatic prompt to
save the current workspace. This is unnecessary in this tutorial,
therefore click NO to continue).
2
Select Double Detection, then click OK.
3
Browse to locate the folder entitled Tutorial I, in the Tutorial data files
folder. Double click on the image Gel 02 Control Cy3 to select the
primary gel image.
4
When the load secondary gel image window appears, double click on
the image Gel 02 Treated Cy5.
10.5.2 Spot detection
1 After the images have been loaded select Process:Process gel images.
2
In the process gel images dialog box which now appears enter the
value 2500 for the estimated number of spots (see help file for an
explanation of this value).
3
Click OK to begin spot detection on the images.
10.5.3 Spot filtering
1 Assign an Area of interest and Filter as described for the controlcontrol gel, using the same filter parameters.
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10.5.4 Setting a threshold
1 Using the Threshold Mode drop-down menu in the Histogram
Selections toolbar of the Histogram View, thresholds can be set to
highlight protein spots that differ in their abundance. For example,
to select spots whose volume ratios differ between samples by
2 fold or more, select 2.0 fold from the drop down menu.
A variety of values in the Threshold Mode box can be selected, such
as 2 S.D. which is based on the model histogram (blue line) in the
Histogram View. Alternatively the threshold mode can be set
manually by selecting Manual and then entering a figure in the
Threshold information box. Enter the 2 S.D. value ascertained in the
control-control gel into the information box. As previously
discussed, this value represents the inherent experimental variation.
It can therefore be hypothesized that spots with ratios which fall
outside this threshold in a control/treated experiment have some
level of significance. As a general rule, the higher the threshold set
the more confidence that differences above the value is significantly
different above experimental variation.
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10.6 Assigning protein of interest
10.6.1 Selecting spots for picking
1 To select those proteins that have been found to be different on the
control–treated gel, select the Protein Filter Dialog icon.
This process can be performed prior to or after spot confirmation.
The amount of time spent on spot confirmation can be reduced by
performing filtering first.
The Protein Filter selects protein spots based on user defined
criteria such as volume ratio, above a maximum volume or peak
height. The protein filter is therefore used as a filter to find proteins
that are interesting which can then be confirmed.
170
2
Open the Protein Filter dialog window. Ensure that the Assign Protein
of Interest check box is selected and that the Assign Pick status is not
selected. This results in the proteins successfully filtered being given
a Protein of Interest status.
3
For the purpose of this tutorial, select to assign protein spots with
a volume ratio greater the 2 SD value from the control-control gel
5
8
and a max volume between 10 and 10 , for example. Click on
Filter to calculate the number of spots that meet the criteria. These
values can be adjusted and the Filter button clicked again to see how
many spots now meet the criteria. The purpose of this filtering is to
narrow down the users search for interesting proteins that are
significantly changing. Click OK.
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4
The subset of spots that met the criteria will now be assigned with
a protein of interest in the protein table (denoted by the letter “I”
in the Function column).
5
Select View:Properties or click on the Properties icon. Go to the Table
View tab and select the Protein of Interest check box, and uncheck all
of the other options. The table view will now display only those
spots assigned as protein of interest in the protein filter. These are
the spots that are of most interest and they can now be manually
confirmed.
10.6.2 Spot confirmation
Three options are available when you start to confirm your spots:
1
Only confirm spots that are Increased (blue) and/or Decreased (red)
in their abundance. This is the recommended option.
Select View:Properties, and go to the Table View tab. Ensure that the
Decreased and Increased options are selected, and that the Excluded
box is not selected. Also ensure that Confirmed is selected,
unconfirmed is not. Click OK.
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2
Only confirm spots that have been assigned with a protein of
interest status (recommended option when the protein of interest
assignment option has been performed).
Select View:Properties, and go to the Table View tab. Ensure that only
the Picked spots checkbox is selected.
3
All spots, Decreased (red), Increased (blue) and Similar (green) are
manually verified. (This takes approximately 1.5 hours for 1000
spots - this is not recommended as only those spots that are
increasing or decreasing in abundance are of interest).
Select View:Properties, and go to the Table View tab. Ensure that the
Similar, Decreased and Increased options are selected, and that the
Excluded box is not selected. Click OK.
When confirming spots, a decision is made whether the spots selected
are true-protein spots or non-protein spots, such as a particle of dust
that has not been removed by the Exclude Filter function. This can be
determined by using the Image View and the 3-D View.
Click Confirm if the spot is a genuine protein spot. The next spot in the
list will now be automatically displayed. If it is non-protein spot, select
Exclude in the Spot Assignment toolbar and then click Confirm.
Continue with this process until you have confirmed all the relevant
spots.
10.6.3 Exporting the Pick List and physically excising spots from the gel
To excise the spots from the gel a separate preparative gel should be
used. This is then matched to this analytical gel set in DeCyder
Differential Analysis Software BVA module and the protein of interest
status is transferred to the relevant matching spots on the preparative
gel. The pick list is based upon these proteins. The pick list with x, y coordinates of spot positions is then exported based on the preparative
gel. The pick list with the physical preparative gel is then taken to Ettan
Spot Picker or Ettan Spot Handling Workstation for spot excision and
later analysis by mass spectroscopy.
Generating a pick list is covered in more detail in Tutorial III.
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Tutorial II - Employing an internal standard to Analyze Protein Changes
11 Tutorial II - Employing an internal standard to
Analyze Protein Changes
11.1 Objectives
Tutorial I describes the analysis of a simple experimental design using
only the DIA module to evaluate samples where an internal standard is
not possible due to sample scarcity. This tutorial extends to a more
complex experimental design incorporating an internal standard with
several replicate samples.
This tutorial describes how to find proteins that exhibit statistically
significant changes between control and treated groups of bacterial
cultures using DeCyder Differential Analysis Software. The procedure
in this tutorial can be applied to any two groups of protein mixtures
with either replicate gels or replicate biological samples. The tutorial
outlines the various stages in experimental design, sample organization,
protein detection and quantitation, gel matching and statistical
analysis.
11.2 Overview
The following describes the various stages involved in identifying all
proteins that are differentially expressed in a given system, and which
therefore warrant further investigation. The stages are:
1
An experimental design is devised that will generate statistically
significant results; a design which minimizes or eliminates in-gel
and gel-to-gel system variation.
2
Eight sample lysates that form the basis of the experiment are
prepared. This consists of four lysates derived from four bacterial
cultures treated with benzoic acid and four control flasks that have
not been treated. Since the gel to gel variation in this system is low,
gel replicates are not compulsory if biological replicates are
available.
3
Aliquots from each sample are taken and pooled to prepare a
standard sample. All three sample types (standard, control and
treated) are labelled with an appropriate CyDye DIGE Fluor
minimal dye (Cy2, Cy3 or Cy5 dye) as described in the table
overleaf. Each sample is then applied to the gels.
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4
The samples are separated within 4 gels by 2-D electrophoresis.
Three images are produced from each gel, by scanning at
appropriate wavelengths for each of the three fluors. The
recommended scanning instrument for this is the Typhoon 9000
series Variable Mode Imager.
5
The images from the first gel are loaded into the DIA module.
6
Spot detection and calculation of the spot properties are performed
for all images from the same gel.
7
Protein spots are normalized using the in-gel linked internal
standard.
8
XML formatted data for the first gel is exported, for subsequent
matching and analysis in the BVA module.
9
This XML data for the first gel and prepared XML data from the
remaining gels are loaded into the BVA module.
10 Spot maps (images) are assigned to groups and the experimental
design is set up in the BVA module.
11 All gels or those gels that require it to aid in the matching process
are landmarked. Matching is then performed.
12 Spots that conform to certain criteria such as statistical significance
and above a certain ratio change are assigned as proteins of interest.
13 These spots are confirmed for eventual inclusion into a pick list.
The individual stages involved in the experiment are now described
more fully.
11.3 Experimental design
Four replicate gels are loaded with bacterial lysates as indicated in the
table below.
Gel number
Gel 1
Gel 2
Gel 3
Gel 4
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Cy2
Standard
Standard
Standard
Standard
Cy3
Control 1
Treated 2
Control 3
Treated 4
Cy5
Treated 1
Control 2
Treated 3
Control 4
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Each gel contains a standard sample to normalize control and treated
samples against. The standard sample is derived from control and
treated lysates, which are pooled in equal concentration.
The set of three images generated from a single gel are sequentially
processed using the DIA module, in order to perform spot detection
and spot quantitation against the internal standard.
The data from DIA analysis is exported as an XML file for further
analysis in the BVA module to evaluate protein abundance differences.
11.4 Spot detection and quantitation
Spot detection and quantitation by normalizing against the internal
standard is performed using the DeCyder Differential Analysis
Software DIA (Differential In-gel Analysis) module. Gels are processed
sequentially in the DIA module.
11.4.1 Selecting gel images
1 Double click the DeCyder Differential Analysis Software DIA icon
on your desktop to open the DIA module. When the user interface
appears, select File:Create Workspace.
2
Select Triple Detection, then click OK
3
Browse to locate the folder Tutorial II in the Tutorial data files folder.
Note:
All the images are named in the style Gel ON Cy X Standard,
Control or Treated.
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4
Select Gel 01 Cy2 Standard, then click Open to load the Primary Gel
Image.
5
Select Gel 01 Cy3 Control, then click Open in the Load Secondary Gel
Image window.
6
Select Gel 01 Cy5 Treated, then click Open in the Load Tertiary Gel
Image window.
11.4.2 Spot detection and quantitation
1 After the images have been loaded select Process:Process Gel Images.
2
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Enter the value 2500 for the estimated no. of spots (see section
3.3.1 for an explanation for this value). Then click OK to begin spot
detection and quantitation on all three images.
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The DIA analysis takes between 3 and 10 minutes depending on the
specifications of the computer.
11.4.3 Viewing the DIA workspace
1 Upon completion of the DIA processing, the spot map data is
displayed in the workspace (as shown below).
The user interface is divided into four main windows that show the
Image View, the Histogram View, the 3-D View and the Table View.
The four views are linked. Clicking on any spot on the Image View
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highlights the spot in magenta in the Histogram View. The spot is
also represented three dimensionally and is highlighted in the Table
View. Each of the four views can be expanded to fill the whole
screen. To do this, click on the View button and the menu below is
displayed.
2
Click on any of the four views available to expand that view to fill
the screen. After expanding all the views in turn, click on View and
select All Views from the menu above to return to the All views
display. Alternatively, use the icons displayed on the main toolbar.
Note:
3
If, after detection has been completed, the Table View
remains empty, click on the Properties icon to bring up the
properties dialog box. Alternatively click on
View:Properties. By default this will open on the
Workspace tab. Change to the Table View options by
clicking on the Table View tab.
The gel images displayed in the image view can be changed using
the pull down menus in the image view title bar.
When using an experimental design that includes an internal
standard, as in this case, it is conventional that the primary image
view stays as the image of the internal standard whereas the
secondary view displays the images of the analytical gels.
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Spot quantitation, expressed as a volume normalized against the
internal standard, is calculated in the DIA module then displayed in
the Table View under the Volume Ratio column. This column is
automatically amended when selecting between analytical gels in
the secondary gel view.
11.4.4 Exporting spot map data
The data associated with the spot boundaries and spot quantitation are
employed by the BVA module to perform inter-gel matching and further
statistical analysis. This data is therefore exported from the DIA
module in an XML format.
1
Select File: Export Spot Maps...
2
Browse to locate the folder entitled Tutorial II in the Tutorial data files
folder. Name the exported file Gel 01, then click Save.
3
The remaining gels must be subjected to similar analyses using the
DIA module. For the purposes of this tutorial, the gels have been
prepared and placed in the Tutorial II file, ready for analysis in the
BVA module.
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11.5 Creating the BVA workspace
11.5.1 Selecting gels
1 Double click the DeCyder Differential Analysis Software BVA icon
on your desktop to open the BVA workspace. Select File:Create
workspace and browse to locate the XML files listed above, in the
folder Tutorial II located in the Tutorial data files folder.
2
Select all four XML files by holding down the shift key and clicking
on each image using the left mouse button then click Open. The
XML and image files will automatically be loaded into the BVA
workspace.
The BVA interface consists of four different modes: Spot Map Table
(ST), Match Table (MT), Protein Table (PT) and the Appearance Table
(AT). Each screen in the BVA interface possesses a similar layout,
consisting of:
• Image View: Displays the gel images making up the experimental set,
with the currently selected spot shown in magenta.
• Table View: Depending on the screen selected, displays either
information on the images used in the experiment, different
properties for a single spot across the gels in the analysis set or
protein spot specific statistical values.
• 3-D View: Representation of the selected spot in 3 dimensions (not
displayed in Spot Map Table mode).
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• Graph View: Graphical representation of the protein abundance data
for a single spot across the different images in the analysis set.
Note: The graph view is only displayed in Protein Table and
Appearance Table (AT) mode. In Spot Map Table (ST) mode the
graph view is substituted for Experimental Design view (see
section 4.5.2).
All the tables in BVA can be ordered by clicking on the headers at the
top of each column.
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Other than the icons to move between the different screens the main
BVA tool bar is identical to the DIA tool bar.
11.5.2 Function assignment
1 Ensure that the Spot Map table icon is selected. This screen consists
of an Image, Experimental Design and Table View. Select View:Table
View or click on the Magnify Table View icon to display the Table View
only. The Table View shows the images that have been loaded into
the workspace and the number of spots that have been detected on
them.
Note:
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A set of images from the same gel will have the same
number of spots since the DIA detection algorithm is
designed to detect the same number of spots on images
from the same gel.
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Under the column Function all the images will be labelled with the
function Analysis (A) and one of the analysis images (the image
with the largest number of detected spots) will be labelled with the
function Master (M). The analysis function designation indicates
that each of the images will be included in post matching statistical
analysis. The Master function can be assigned to a different image,
by selecting the image to be assigned as master using the left mouse
click. Tick the box entitled Master at the bottom of the table in the
area entitled Function for Spot Map.
11.5.3 Experimental group assignment
To generate statistical data for the proteins which differ between the
control and treated groups it is necessary to identify the different
experimental groups within the software. The three groups involved in
this experiment are Control, Treated and Standard. Group assignment
is performed via the Experimental Design view.
1
Select View:Experimental Design view or select the Magnify Experimental
Design View icon.
2
The standard images will automatically be assigned to the group
Standard by the software, if the word standard or std appears in the
image name. The standard images will therefore be located in the
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folder entitled standard. By default non-standard spot maps are
assigned as Unassigned. To assign the control and treated images to
their respective groups, these groups must first be created.
To create a control group select Add button, then enter Control in the
Group text box and click Confirm. The new group folder, Control is
subsequently displayed. The treated group is similarly generated.
Spot maps are assigned to either the control or treated group
folders by selecting the Unassigned folder, then dragging and
dropping spot maps from the center panel of the Experimental
Design view to the appropriate folders on the left of the screen.
11.5.4 Landmarking
Landmarking allows the user to manually define matched protein spots
in order to improve the accuracy of the gel-to-gel matching process,
when the samples are particularly complex or the gels have not run
well.
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1
In some cases images from different gels may vary sufficiently to
require landmarks to be set. To set a landmark, select the Match
Table (MT) by clicking the MT icon.
2
Click on the Magnify Image View icon to display the gel images only.
The image on the left represents the Master image while the image
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on the right represents one of the images to be matched to the
master.
3
To begin setting landmarks click on the button entitled Landmark
mode, at the bottom of the screen (this button will now become
yellow). From the Select Match Gel scroll down menu (circled in the
following image), select the first image.
All the spots should appear red to indicate that they are unmatched.
If you select an image and the spots appear green, both images are
from the same DIA image analysis and thus landmarks are not
necessary, therefore select another image.
4
If the spots on the images are not clearly visible it may be necessary
to alter the contrast/brightness settings of the images. Click on the
contrast/brightness icon and alter the position of the bars to alter the
contrast and brightness of the images until only the most intense
spots are visible.
5
To set a landmark click on a clearly defined spot on the master
image (the spot boundary should become magenta). Now select the
spot on the match image which corresponds to the master image
spot, so that this spot becomes magenta, a few seconds later a
vector line should appear showing that the spots have been
matched and the landmark has been set.
6
Repeat this procedure until approximately 10 landmarks have been
set. It is recommended that landmarks are evenly distributed across
the image as this aids the matching process. After landmarks have
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been set on the first image, select the second spot map from the
Select Match Gel scroll down menu.
The landmarks which were set on the previous match image will be
colored yellow on the Master image, thus the same landmarks can
be set on the next match standard spot map by finding the spots
which correspond to the yellow master image spots.
Note:
7
Landmarks are only required on the standard image from
a set of images that have been simultaneously processed in
a DIA analysis.
Repeat this procedure until landmarks have been set on all the
standard images from each DIA analysis. When landmarking is
completed click on the Landmark mode button to exit the landmark
mode. Click on the Match icon, ensure that Match all is selected and
click Match to commence the matching process.
Note:
It is usually only necessary to set landmarks on those
images that differ significantly from the Master image. In
such cases it may be necessary to set more landmarks. In
addition landmarks may have to be set after matching if the
number of matched spots is low or if there are a high
number of wrongly matched spots, in which case matching
will have to be repeated.
11.6 Assessing matching
1
Double click on the Match Table icon or select View:Match Table.
This screen displays data on the gel to gel matching of the different
protein spots and allows match confirmation
The match table consists of an Image View, 3-D View and Table
View. Each of the views are linked, in such a way that selecting a
spot in the Image View will display that spot in 3 dimensions and
highlights that spot in the table.
The matches in the Table View are of two types, Auto Level 1 (high
probability matches) and Auto Level 2 (lower probability matches).
By default the two types of matches are displayed differently in the
Image View. Auto level 1 and Auto level 2 spots are represented by
a darker and lighter spot boundary, respectively. The boundaries of
the unmatched spots are colored red (these colors can be altered by
clicking on the properties icon and selecting the colors tab. The
Colors window allows the user to define colors to the different
matches).
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The vector lines in the Image View indicate the positional difference
for the same protein spot on different gels. The accuracy of the
matching process can now be checked within the match table.
2
Order the table by clicking on the column header Type so that all the
Auto Level 1 matches are at the top of the table.
3
To confirm a match in the Match Table View click on the first spot
in the table (ensure that it is an actual protein spot). The spot
should be selected in the Image View in magenta. Zoom into the
area of the selected spot by holding down the left mouse click and
drawing a square around the selected area.
4
Decide whether the spot in the match image corresponds to the spot
on the Master image. Deciding whether a match is accurate can be
aided by viewing the selected spot and the surrounding cluster in
the 3-D View as well as looking at the matched spots in the Image
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View. If the match is correct, change the Match gel image using the
Select Match Gel scroll down menu to test the matching accuracy on
the other gel images.
5
If the match is incorrect, click on the Break Match button (at the
bottom of the screen), which now becomes Add Match. Click the
correct spot on the match image.
6
If the spot on the match image has also been wrongly matched
break this match as before. Select the corresponding spots on the
master image and the match image and click on the button entitled
Add Match.
7
This procedure can be carried out, for example, using 5 randomly
chosen Level 1 spots and 10 level 2 spots (these spots should be real
protein spots). If the matches are accurate in each case move to the
Protein Table (PT).
(If the majority of these 15 matches were incorrect, it may be
necessary to perform landmarking and a further round of
matching.)
The purpose of the above step is to get an overall impression of the
matching accuracy. This can be done in several ways. One way is to
look at the match vectors for each gel, if the match vectors are not
oriented in the same direction over the gel, this may indicate an area of
mismatches. For example if in one area of a gel the match vectors are
at right angles to the other match vectors on the gel then this area
should be landmarked and re-matched.
Rather than choosing a random auto level 1 match and flicking through
each gel for that protein on a gel by gel basis, click on random spots on
the image, spread around the gel and see if they are correctly matched.
If the majority are matched correctly then you can move on to the next
gel.
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11.7 Post-matching landmarking
If rematching is necessary, a post-matching landmark procedure can be
performed.
1
The landmarking process here is similar to landmarking before
matching. To set the first landmark select the first spot to be set as
a landmark on the master image. If the spot selected on the master
image is incorrectly matched, click on Break Match. Then select the
spot on the match image that corresponds to the correct match on
the master image. If this spot is also incorrectly matched click on
Break Match. Now select the two corresponding spots so that both
spots become magenta and click the Add Match button.
Note:
It is only necessary to set landmarks on those images which
are poorly matched. The number of landmarks set depends
on the accuracy of matching.
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11.8 Statistical analysis
1
Click on the Protein Table icon or select View:Protein Table. The
Protein Table lists each of the matched spots, across all the images
in the experiment. The Protein Table mode consists of an Image,
3-D, Graph and Table View. Each of the views are linked, in such a
way that selecting a spot in the Image View will display that spot in
three dimensions, highlight that spot in the table and graphically
display the abundance values of that spot across all the images on
which the spot occurs.
The Protein Table mode allows you to view those proteins that
show a significant difference in expression between the control and
the treated group. The statistical level of significance is calculated
using the T-test (if more than two groups, the ANOVA test is used).
2
To perform statistical calculations select Process:Protein statistics. In
the box entitled population 1 select Control, in the box entitled
population 2 select Treated. Ensure that the Average Ratio and
Student’s T-test boxes are selected as below. Click Calculate.
The spot specific statistics along with the average spot volume ratio
now appear in the table.
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The statistical calculations are performed using the null hypothesis
that the differences in protein standardized abundance (i.e. the
abundance relative to intra-gel standard and/or biological variation
within groups) of control and treated samples is not significant,
therefore a low value indicates a high level of significance.
3
Click on the T-test column header to order the table so that the
lowest value is at the top of the table. Click on the spot with the
lowest value in the Table View. This spot will now be displayed in
all four views.
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4
To alter the Graph View open the Properties window. Click on the
Graph View tab and ensure that the view parameters are identical to
those shown below. Click OK.
In the Graph View, the dashed lines link data points from sample
spots to their respective standard data point. Therefore, in this
experiment there are four matched control spots and four matched
treated spots (associated with four standard data points).
The standards are the same on each gel, and hence the
normalization calculates how samples change with respect to their
in-gel standard. A ratio of sample to standard of for example 2.5,
means that the sample protein is 2.5 times greater than its standard
protein, or a ratio of 2.5 to 1. When this methodology is used, all
standards are 1. Thus the standard is displayed graphically as 1 and
the samples displayed relative to their standard. The log of 1 is zero
and therefore when the log standardized abundance is displayed, all
standards are zero.
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The protein table shows average ratio (i.e. Average abundance of
control/Average abundance of treated) as well as the statistical
significance of the differences.
11.9 Assigning spots as proteins of interest
11.9.1 Selecting proteins of interest
This process can be performed prior to spot confirmation or after spot
confirmation. In some ways, doing this first reduces the amount of time
spent on spot confirmation.
1
To select those proteins that have been found to be statistically
different on the analytical gels, select the Protein Filter Dialog icon.
The Protein Filter selects protein spots based on user defined
criteria such as Student's T-test value, average ratio or volume. The
protein filter is therefore used as a filter to find proteins that are
interesting which can then be confirmed.
2
Ensure that the Assign Proteins of Interest check box is selected and
that the Assign Pick status is not selected. This results in the proteins
successfully filtered being given a Protein of Interest status. For the
purpose of this tutorial, assign for selection protein spots with a
T-test score less than 0.01 and with an average ratio greater than or
equal to 1.5 or less than or equal to -1.5 and a gel volume between
105 and 108. Click on Filter and it will tell you how many spots
meet the criteria. These values can be adjusted and the filter button
clicked again to see how many spots now meet the criteria. The
purpose of this filtering is to narrow down the users search for
interesting proteins that change significantly. Click OK.
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Note:
3
The volume filtering criteria is applied to spot volumes on
the Master gel. If a preparative gel is assigned the
parameters can be applied to this pick gel (see tutorial III).
The subset of spots that meet the criteria will now be assigned with
a protein of interest status in the protein table.
Alternatively, individual spots can be assigned manually by clicking
on the spot of interest and clicking in the Protein of Interest check
box at the bottom of the Protein Table screen.
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4
Select View:Properties or click on the Properties icon. Go to the
Protein Table tab and check the Protein of Interest (I) only box. Click
OK. Now in the Protein Table are only those spots that are of most
interest and can now be manually confirmed.
11.9.2 Spot confirmation in Protein Table
A spot should be confirmed when a significant difference has been
studied, and the user is certain that it is a real protein spot and it has
been correctly matched on all gels.
1
To confirm the first spot in the Protein Table, select the spot in the
Table View. The spot is automatically selected in all the other views.
In the Secondary image scroll down menu above the image, select the
image that corresponds to the Master image. Zoom into the area of
the spot in the Image View as described previously to determine
whether the correct spots have been matched correctly between the
primary and the secondary images. If this is the case select another
image using the Primary image scroll down menu and repeat the
match assessment.
2
If the protein spots are incorrectly matched move to the Match
Table View by clicking MT and alter the match as described
previously.
3
When all the matches are correct the protein spot can be confirmed
by clicking the Confirm button. This records that the spot has been
manually checked.
This confirmation should be done for all the significant differences that
have been assigned with a protein of interest status. If after manual
scrutiny the user decides that the spot should not be selected, deselect
the protein of interest status for the given spot by checking the box
marked Protein of Interest at the bottom of the Protein Table.
11.9.3 Exporting the Pick List and physically excising spots from the gel
To excise the spots from the gel a separate preparative gel should be
used. This is matched to the analytical gel set and the pick status is
eventually transferred to the relevant matching spots on the preparative
gel. The pick list with x, y co-ordinates of spot positions is then
exported based on the preparative gel. The pick list along with the
physical preparative gel is then taken to Ettan Spot Picker for spot
excision and later analysis by mass spectrometry. For a detailed
description of the workflow associated with pick list generation, see
Chapter 3.
Generating a pick list is covered in more detail in Tutorial III.
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12 Tutorial III - Processing the Preparative Gel
and Generating a Pick List
12.1 Objective
As can be seen in tutorials I and II, proteins of interest, which warrant
identification and further investigation, can be obtained in both the
DIA and BVA modules. The Protein Filter tool is applied to the
analytical gels to rapidly ascertain the proteins that are affected by the
experimental conditions. When using either CyDye DIGE Fluor
minimal dyes or CyDye DIGE Fluor saturation dyes, the highlighted
proteins of interest have to be matched to a preparative gel, from where
the protein spots can be directly excised using Ettan Spot Picker or
Ettan Spot Handling Workstation (see Ettan DIGE system user
manual). The workflow for generating a pick list from a preparative gel
in both CyDye DIGE Fluor dye types is identical. Here in tutorial III a
preparative gel for a CyDye DIGE Fluor minimal dye experiment is
used to demonstrate the various steps in generating a pick list.
The aim of this experiment is to generate a pick list, on a SYPRO
stained gel, relating to the most significant differentially expressed
proteins in control and treated bacterial lysates determined from Ettan
DIGE system analytical gels.
When using the CyDye DIGE Fluor minimal dyes, a SYPRO stained
pick gel is required rather than picking directly from an Ettan DIGE
system gel because the CyDye DIGE Fluor minimal dye label only 5%
of the protein. This results in the majority of the (unlabelled) protein
migrating slightly different and hence not ending at exactly the same
place as the (labelled) protein seen on the gel. Therefore a post stain
(total stain) is used on a preparative gel for spot excision. The image
generated is then matched to the analytical set. The positions of the
significant differences are transferred to the SYPRO stained gel and a
pick list is created. This pick list, along with the SYPRO stained gel, is
transferred to the automated Ettan Spot Picker or Ettan Spot Handling
Workstation for spot excision and subsequent digestion, spotting and
mass spectrometry analysis.
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12.2 Overview
1
This Tutorial follows on from Tutorials I and II. The first two
tutorials concentrate on finding differences between different
biological groups. This tutorial describes how to excise these spots
for further protein identification analysis.
2
An experimental design is determined as described in tutorial II,
with the addition of a separate preparative gel containing two
reference markers and stained with SYPRO Ruby.
3
The analytical gels are analyzed in DeCyder Differential Analysis
Software to ascertain the proteins of interest, as described in
Tutorial I.
4
The preparative gel is analyzed in DeCyder Differential Analysis
Software DIA. The reference markers are assigned in DIA
(alternatively this can be done in BVA).
5
The preparative gel spot map is exported as an XML file and added
to the BVA workspace that contains the analytical gels.
6
The preparative gel spot map is landmarked and matched to the
master gel of the analytical set.
7
The protein of interest assignments are transferred from the
analytical set to the preparative gel.
8
The proteins of interest are assigned a Pick status, then the pick
locations are visually inspected (and edited if necessary).
9
A pick list is exported from the preparative gel and the gel with pick
list is then taken to Ettan Spot Picker or Ettan Spot Handling
Workstation for spot excision (See the relevant User Manual for
instructions).
12.3 Experimental design
Ettan DIGE system and DeCyder Differential Analysis Software are
used to investigate differential protein expression in experimental
groups. These identified proteins can then be picked from a preparative
gel for digestion and subsequent mass spectrometry analysis.
Gels 1 to 4 were generated to ascertain those proteins that were
differentially expressed in control and treated bacterial samples. These
gels have been pre-analyzed in DeCyder Differential Analysis Software
and saved as a BVA file.
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A SYPRO Ruby stained preparative gel (gel 5), loaded with both
control and treated samples was prepared for spot picking. The spots
on this gel are first detected within the DIA module, then matched to
the analytical gels (gels 1 to 4) in DeCyder Differential Analysis
Software BVA.
Gel number
Gel 1
Gel 2
Gel 3
Gel 4
Gel 5
Cy2
Standard
Standard
Standard
Standard
Cy3
Control 1
Treated 2
Control 3
Treated 4
–
Cy5
Treated 1
Control 2
Treated 3
Control 4
–
SYPRO Ruby
Control + Treated
12.3.1 Identifying protein spots on SYPRO Ruby stained gel
1 Double click the DeCyder Differential Analysis Software DIA icon
on your desktop to open the DIA module.
2
Select File:Create Workspace.
3
Select Single Detection, then click OK.
4
Browse to locate the folder entitled Tutorial III in the Tutorial data files
folder. Select the SYPRO gel image, then click Open to load the
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image.
5
As the image was stained with the SYPRO Ruby stain the initial
image contrast may require adjusting. Select the contrast/brightness
icon and alter the bars to make the image clearer.
12.3.2 Spot detection
1 After the image has been loaded select Process:Process Gel Images.
2
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In the Process Gel Images dialog box which now appears enter the
value 2500 for the estimated number of spots (see section 3.3.1).
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Select the Autodetect Picking references check box. Click OK to begin
spot detection on the images.
Before going any further, it is important to become acquainted with the
main tool bar.
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The user interface is divided into four main windows, the Image View,
the Histogram View, the 3-D View, and the Table View.
The four views are all linked. Clicking on a spot on Image View
highlights the spot in magenta in the Histogram View (the Histogram
View is disabled when analyzing single images such as SYPRO stained
gel images). The spot is also represented three dimensionally and is
highlighted in gray in the Table View.
3
To display the entire gel image, click on the Fit to window icon.
The detected spots are of three types: increased, decreased and
unchanged in expression (colored blue, red and green, respectively,
in the Histogram View).
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4
If, after detection (the hourglass turns back to a cursor) the Table
View remains empty, click on the Properties icon to bring up the
Properties dialog box. By default this will open on the Workspace
tab.
5
Change to the table options by clicking on the Table View tab.
6
By selecting and deselecting the check boxes the various categories
of spots can be selectively displayed. Deselecting all the check boxes
results in the Table View being blank. Similarly, spots can be
selectively displayed in the image view using the Spot Display tab.
The Table View tab works on OR logic, i.e a spot only needs to
conform to one criterion to be shown in the table.
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Selecting different protein spots on the images shows that some of the
spots that have been detected are in fact either dust particles or artifacts
from the gel. To remove these artifacts an Exclude Filter must be used
on the images.
12.3.3 Assigning an area of interest
Due to gel heterogeneity at the edges of the images, there are artifacts
that need to be removed. These can be removed by setting an area of
interest. This function only works with the filter. If an area of interest
was set before detection all the spots on the image will still be detected
(even those outside the area of interest).
1
Click on the Fit to window icon on the tool bar to fit the image to the
Image View.
2
Click on the Image view icon on the tool bar to have a full screen
view of the gel image.
3
Next, click on the Properties icon to bring up the properties window.
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4
Select the Spot Display tab and deselect Similar, Increased and
Decreased, so that the check boxes are identical to those shown in
the figure below. Click OK.
5
To set an area of interest select Edit:Define area of interest. Using the
rectangular target pointer which now appears drag the mouse to
draw a rectangle around the gel, ensuring that edge artifacts are
removed. It does not matter if the Reference Markers are inside or
outside the Area of Interest as these are set manually.
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6
Click on the Properties icon to bring up the properties window.
Select the Spot Display tab, reselect the Similar, Increased and
Decreased options and click OK.
7
Select Process:Exclude Filter and use the same parameters to filter the
images again.
All the spots on the outside of the area of interest will automatically
be removed.
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12.3.4 Gel artifact removal
A 3-D View clearly shows if a detected spot is a gel artifact rather than
a protein spot due to the very steep sides and pointed top of an artifact
compared to the smooth curve of a protein spot.
In order to exclude these artifacts from subsequent analysis a set of
Exclude filter parameters must be generated.
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1
In the Table View click on the column header Area until the spot
with the smallest area is present at the top of the table.
2
Scroll down the table from one spot to another until the next spot
in the table is an actual protein spot and not a dust particle or gel
artifact. Make a note of the Area value of the artifact immediately
before the real protein spot. This value will be used for the Area
category in the Exclude filter.
3
Repeat this procedure for Volume.
4
When both values have been found, select Process:Exclude Filter to
display the Exclude Filter dialog box. Select the Area and Volume
check boxes and enter the found values. Click OK.
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An example of a non-stringent set of filter parameters that can be used
to run a light filter is as follows:
Area
Volume
50
20000
5
Select the All views icon to display all four views.
6
Export the data from the DIA file (as an XML file) for gel to gel
matching in BVA by selecting File:Export Spot maps.
7
Save the exported file in the folder entitled Tutorial III in the Tutorial
data files folder.
At this point the DIA workspace can be closed (without saving the
workspace).
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12.4 Matching to analytical gels
The SYPRO Ruby stained picking gel has to be matched to analytical
gels in order to identify the proteins on the picking gel that show
differential expression and hence require picking.
12.4.1 Preparing the workspace
1 Double click the DeCyder Differential Analysis Software BVA icon on
your desktop to open the BVA workspace.
2
Select File:Open workspace and browse to locate the BVA file entitled
Ecoli Control treated_for picking, in the folder Tutorial III in the Tutorial
data files folder.
3
Double click on the BVA file to open the workspace (this may take
a few minutes depending on the specifications of the computer).
This BVA file contains the experiment described in the
experimental design section of this tutorial. This file has been preanalyzed in the BVA module and several proteins that demonstrate
a statistically significant change upon treatment have been given a
Protein of Interest status and hence will require picking and
subsequent identification.
Note:
If this tutorial is being performed directly after completing
Tutorial II, the created tutorial II workspace can be used
instead of the pre-analyzed BVA file.
The BVA interface consists of four different modes, Spot Map Table
(ST), Match Table (MT), Protein Table (PT) and the Appearance Table
(AT). Each screen in the BVA interface possesses a similar layout,
consisting of:
• Image View: Displays the gel images making up the experimental set,
with the currently selected spot shown in magenta.
• Table View: Depending on the screen selected, displays either
information on the images used in the experiment, different
properties for a single spot across the gels in the analysis set or
protein spot specific statistical values.
• 3-D View: Representation of the selected spot in 3 dimensions.
• Graph View: Graphical representation of the protein abundance data
for a single spot across the different images in the analysis set.
All the tables in BVA can be ordered by clicking on the headers at the
top of each column.
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Other than the icons to move from one mode to another the main tool
bar is very similar to that seen in DIA.
4
Ensure that the Spot Map Table icon is selected. This screen consists
of an Image and a Table View.
5
Select View:Table View or click on the Magnify Table View icon to
display the Table View only. The table shows the images that have
been loaded into the workspace and the number of spots that have
been detected on them.
Note:
A pair of images from the same gel will have the same
number of spots since the DIA detection algorithm is
designed to detect the same number of spots on image pairs
from the same gel.
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Under the column Function all the images will be labelled with the
function Analysis (A) and one of the analysis images (the image
with the largest number of detected spots) will be labelled with the
function Master (M). The analysis function designation indicates
that each of the images will be included in the post matching
statistical analysis.
6
The Master function can be assigned to a different image, by
selecting the image to be assigned as master using the left mouse
click. Tick the box entitled Master at the bottom of the table in the
area entitled function for spot map. However, the Master should
not be changed here or all the matching will have to be repeated.
7
To add the SYPRO stained preparative gel spot map to the BVA
workspace select File:Import Spot Map(s) and double click on the
XML file exported from the DIA workspace of the Preparative gel.
Once loaded, the SYPRO gel spot map can be assigned as a Pick gel,
ensuring that the eventual pick list will based on the spot co-ordinates
of this SYPRO gel image. Select the SYPRO spot map in the table view
then select the Pick check box in the data control panel. De-select the
default Analysis check box.
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12.4.2 Editing picking reference markers
The software, that controls the picking robot requires two reference
points in order to locate the spots for picking from the preparative gel.
The reference markers placed on the preparative gels (seen as perfect
circles on the images) act as these reference points.
The position of the reference markers are automatically detected during
the spot detection process. However, it is advisable to review the
position of the reference markers and edit them if necessary.
1
Zoom into the area of this reference marker by holding down the
left mouse click to draw a rectangular area around the marker.
2
The size of the target can be altered by clicking on the properties
icon and selecting the Image View tab. A value of approximately 30
should be entered in the Primary Image View box of the picking
references area of the window.
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The position of the target can be altered by selecting Edit:Edit
picking Reference. Place the hand shaped cursor that now appears
on the target and hold down the left mouse click. Move the target
so that it fits exactly around the reference. Magnifying the area of
the gel around the reference marker by selecting the Zoom in icon
may make accurate alignment easier. After reviewing the left
reference marker, zoom into the right reference marker then check
and edit its position in an identical manner. Select Edit:Edit picking
References to exit this mode.
When the picking references have been reviewed it is necessary to
match the preparative gel to the master image, thereby matching
the preparative image to all the analytical images.
12.4.3 Matching and landmarking
1 To match the images first move to the Match Table, by clicking on
the Match Table icon.
From the images it is clear that the SYPRO image is slightly
different from the analytical images. It is therefore necessary to set
manual matches known as landmarks. Landmarking allows the
user to manually define matched protein spots in order to improve
the accuracy of the gel-to-gel matching process.
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2
Click on the Magnify Image View icon to display the gel images only.
The image on the left represents the Master image while the image
on the right represents one of the images to be matched to the
master. Ensure that the image on the right is the SYPRO image
using the Select Match Gel scroll down menu.
3
To begin setting landmarks, click the button entitled Landmark
mode, at the bottom of the screen (this button will now become
yellow). All the spots should appear red to indicate that they are
unmatched.
4
If the spots on the images are not clearly visible it may be necessary
to alter the contrast/brightness settings of the images. Click on the
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contrast/brightness icon, ensuring the Apply to all gel images check box
is not selected. Alter the position of the bars to alter the contrast
and brightness of the images until only the most intense spots are
visible.
5
To set a landmark, click on a clearly defined spot on the master
image (the spot boundary should become magenta). Now select the
spot on the match image which corresponds to the master image
spot, so that this spot becomes magenta. A few seconds later a
vector line should appear showing that the spots have been
matched and the landmark has been set.
6
Set approximately 10–20 landmarks on the images, spread evenly
across the images. As the images are slightly different it will be
necessary to scroll the two images separately. This can be done by
clicking on the Properties icon and selecting the Image View tab.
Deselect the Link image views when scrolling option.
7
After landmarking click on the Match icon and select the Match
Pending and Landmarked option in the window which now appears.
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Click Match.
8
When matching has completed, the vector lines which appear will
be very long due to the difference in size of the analytical and
preparative gel. These vector lines can be hidden by clicking on the
properties icon and selecting the Image View tab. Deselect the
Match vectors when Match Table is displayed option.
9
It is only necessary to determine whether the matches between the
master image and the preparative image are accurate. The only
matches that need to be confirmed are those that match to proteins
assigned with a pick status. Therefore, instead of confirming
matches in the match table, they should be confirmed in the protein
table.
10 Click on the PT icon. Go to the Protein Table tab of the properties
dialog box and select the Protein of Interest (I) only check box.
Click OK.
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11 In the table only proteins assigned as protein of interest will be
present. In the Image View have the master gel as the primary image
and the preparative gel as the secondary image. Start at the top of
the table and click on the first spot, check if this spot is matched to
the master, and if so, whether it is matched correctly. If this is the
case, select the Pick check box in the data control panel. However,
if there is an incorrect match, switch to the Match Table and correct
the match. Then move back to the protein table and look at the
next spot in the list. If the spot is not present on the preparative gel
and hence cannot be picked move to the next protein in the table
(this protein will not have a Pick status). Whilst reviewing the
match accuracy of the preparative gel, the pick locations should be
verified simultaneously.
12 Spots assigned with a Pick status have a yellow transparent cylinder
and a yellow circle denoting the picking location in the 3-D and
image views, respectively. The picking location of all picks is
reviewed to assess whether the pick location requires editing (see
section 5.6).
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13 To edit a pick location, zoom in on the selected spot in the image
view. Select Edit:Edit Pick Location then place the hand shaped cursor
that appears in the image view over the centre of the pick locations
that require editing, then drag the pick circle to the desired location.
Select Edit:Edit Pick Location to exit this mode. To return pick
locations to their original position select Edit:Restore Default Pick
Locations.
12.4.4 Exporting the Pick List
Now that the SYPRO stained gel has been matched in and the pick
proteins have been reviewed, the Pick list has to be exported for use on
the appropriate Ettan picking system.
Select File:Export Picking List from and select the Pick Spot Map to export
a pick list. Type in an appropriate file name for the pick list. The pick
list can be saved as either a text file or an XML file by appending the
appropriate file extension to the file name (.txt or .xml). The text and
XML file are used for Ettan Spot Picker or Ettan Spot Handling Work
station, respectively. Click OK.
The pick list file and the actual SYPRO Ruby stained gel are taken to
the appropriate picking system. The pick list contains the relevant
information to instruct the picker to excise the spots. For more details
refer to the relevant User Manual.
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13 Tutorial IV - Fully automated identification of
differentially expressed proteins
13.1 Objective
This tutorial describes how to find statistically significant proteins that
are differentially expressed between control and treated groups of
bacterial cultures using DeCyder Differential Analysis Software in a
fully automated manner. The methodology of this tutorial can be
applied to any two groups of protein mixtures with either replicate gels
or replicate biological samples. The tutorial outlines the various stages
in experimental design, sample organization, protein detection and
quantitation in a gel, gel matching and statistical analysis.
13.2 Overview
The following list describes the various stages involved in identifying all
the proteins that are differentially expressed in a given system, and
which may therefore be worthy of further investigation.
The stages are:
1
An experimental design is devised that will generate statistically
significant results; a design which minimizes or eliminates in-gel
and gel-to-gel system variations.
2
Eight sample lysates that form the basis of the experiment are
prepared. This consists of four lysates derived from four bacterial
cultures treated with benzoic acid and four control flasks that have
not been treated. Since the gel to gel variation in this system is low,
gel replicates are not compulsory if biological replicates are
available.
3
Aliquots from each sample are taken and pooled to prepare a
standard sample. All three sample types (standard, control and
treated) are labelled with an appropriate CyDye DIGE Fluor
minimal dye (Cy2, Cy3 or Cy5 dye) as described in the table
overleaf. Each sample is then applied to the gels.
4
The samples are applied to the four replicate Ettan DIGE system
gels, and the gels are then run. Three images are produced from
each gel, by scanning at appropriate wavelengths for the three
fluors. The recommended scanning instrument for this is the
Typhoon Variable Mode 9000 series Imager.
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Two representative images from a single gel are initially processed
using DeCyder Differential Analysis Software DIA (Differential Ingel Analysis) module. The DIA module performs spot detection
using all images loaded, generating spot information for all images
simultaneously (in this case standard, control and treated images).
6
The data is examined to identify appropriate values for a number
of variables that are set in the Exclude filter (or artifact filter).
These values are automatically applied later in the experiment to
remove all non-protein spots from all the gel images.
7
The Batch Processor is run to perform a fully automated
comparative analysis of the control and treated samples. This
analysis involves the following steps:
• Running a series of four sequential DIA analyses (for each of the
four gels).
• Performing spot detection and calculation of the spot properties.
• Removing non-protein spots using the previously ascertained
exclusion filter parameters.
• Normalization of the protein spots using the in-gel linked
internal standard.
• Loading of the derived spot maps and associated data into the
BVA module.
• Inter-gel matching of spot maps.
• T-test analysis of proteins in control and treated samples.
• Generation of a pick list for Ettan Spot Picker.
13.3 Experimental design
In order to generate statistically valid data, a minimum of three
replicates must be used. These can be either biological replicates from
each group or, if no biological replicates are available then replicate gels
must be employed. For biologically relevant answers to a hypothesis, it
is strongly advised that biological replicates of each group are used. The
greater the number of biological replicates, the more biological
variation is taken into consideration and therefore, the more
biologically relevant the results are to the system being investigated. For
the purpose of this tutorial, we describe the analysis of four replicate
gels loaded with bacterial lysates as indicated in the following table.
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Each gel contains a standard sample to allow normalization of all
control and treated samples. The Standard sample is obtained by
mixing aliquots of control and treated lysates, pooled in equal
concentration.
Gel Number
Gel 1
Gel 2
Gel 3
Gel 4
Gel 5
Cy2
Standard
Standard
Standard
Standard
Cy3
Control 1
Treated 2
Control 3
Treated 4
Cy5
Treated 1
Control 2
Treated 3
Control 4
SYPRO Ruby
Control+Treated
The data analysis is performed in two parts, using the various DeCyder
Differential Analysis Software modules:
• Determining the protein spot filtering parameters.
• Fully automated multi-gel processing using the Batch Processor
module.
13.4 Protein spot filtering (optional)
In order to identify and exclude artifacts from subsequent analyses, the
DIA module is initially run on a representative pair of images from the
same gel. This is performed using the DeCyder Differential Analysis
Software DIA (Differential In-gel Analysis) module, which identifies
protein spots and allows subsequent filtering to remove dust particles
and other gel artifacts. The user can examine the results, and specify a
series of parameters that will automatically filter all spots that fail to
meet the set parameters. This step is optional. Analyses can be
performed without filtering workspaces to remove dust particles.
It is possible to omit filtering because a dust particle on a specific gel is
very unlikely to be in the same position in other gels and therefore will
not be matched and will not be in the final analysis. However, filtering
does “clean up” the gel and may make the matching algorithm faster
with possible greater accuracy.
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13.4.1 Selecting gel images
1 Double click the DeCyder Differential Analysis Software DIA icon
on your desktop to open the DIA module. When the user interface
appears, select File:Create Workspace.
2
Select Double Detection, then click OK.
3
Browse to locate the folder Tutorial IV in the Tutorial data files folder.
Note:
4
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All the images are named in the style Gel 0X Cy X Standard,
Control or Treated.
Double click on image Gel 01 Cy2 Standard to load the Primary Gel
Image.
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When the Load Secondary Gel Image window appears, double click
on the image Gel 01 Cy3 Control. At this point the two images are
displayed in the top left area of the screen. Having loaded the
images, the next stage is to perform Spot Detection.
13.4.2 Spot detection
1 After the images have been loaded select Process:Process Gel Images.
2
When the Process Gel Images menu option is selected, the following
dialog box appears:
3
Enter the value 2500 for the Estimated no. of spots (see help file for
an explanation of this value). Then click OK to begin spot detection
on the images. The software then processes the two images and
calculates a series of values for each spot on the two images. The
DIA analysis takes between 1 and 5 minutes, depending on the
specifications of the computer.
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Before going any further, it is important to become acquainted with the
main tool bar.
4
When the DIA analysis has finished, the screen below appears.
The user interface is divided into four main windows which show
the Image View, the Histogram View, the 3-D View and the Table
View.
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The four views are linked. Clicking on any spot on the Image View
highlights the spot in magenta in the Histogram View (the
Histogram View is disabled when analyzing single images such as
SYPRO stained gel images). The spot is also represented three
dimensionally and is highlighted in gray in the Table View.
Each of the four views can be expanded to fill the whole screen. To
do this, click on the View button and the menu below is displayed.
5
Click on any of the four views available to expand that view to fill
the screen. When you have finished, click on View and select All
Views from the menu above to return to the All views display.
Alternatively, use the icons displayed on the main toolbar.
6
If, after detection has been completed (the hour glass turns back to
a cursor), the Table View remains empty, click on the Properties icon
to bring up the Properties dialog box. Alternatively click on
View:Properties. By default this will open on the Workspace tab.
7
Change to the Table View options by clicking on the Table View tab.
8
By selecting and deselecting the check boxes the various categories
of spots can be selectively displayed. Deselecting all the check boxes
results in the Table View being blank. Similarly, spots can be
selectively displayed in the Image View using the Spot Display tab.
The Table View tab works on OR logic, i.e. a spot only needs to
conform to one criterion to be shown in the table.
Select the Similar, Decreased and Increased check boxes, as indicated
on next page.
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Selecting different protein spots on the images shows that some of
the spots that have been detected are in fact either dust particles or
artifacts from the gel. To remove these artifacts an Exclude Filter
must be used on the images.
13.4.3 Gel artifact removal
A 3-D View clearly shows if a detected spot is a gel artifact rather than
a protein spot due to the very steep sides and a pointed top of an artifact
compared to the much smoother curve of a protein spot.
In order to exclude these artifacts from subsequent analysis a set of
Exclude Filter parameters must be generated. This is the main reason
for running the DIA module at this point.
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1
In the Table View click on the column header Area until the spot
with the smallest area is present at the top of the table.
2
Scroll down the table from one spot to another until the next spot
in the table is an actual protein spot and not a dust particle or gel
artifact. Make a note of the Area value of the artifact immediately
before the real protein spot. This value will be used for the Area
category in the Exclude filter.
3
Repeat this procedure for Volume.
4
When both values have been determined, select Process:Exclude
Filter to display the Exclude Filter dialog box. Select the Area and
Volume check boxed and enter the found values. Click OK.
An example of a non-stringent set of filter parameters that can be used
to run a light filter is as follows:
Area
Volume
50
20000
The four gels used in this experiment were run under identical
electrophoretic conditions, therefore the filter parameters generated for
a representative pair of images performs equally effectively on the
images from the other gels in the experiment. If comparing gels from
different runs it may be wise to perform a single DIA analysis for each
run and set filter parameters accordingly. Once the gel artifacts have
been successfully filtered, note the exclude filter parameters then close
the DIA module.
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13.5 Processing multiple images
Detection and subsequent matching of protein spots from the images
derived from the four gels must now be performed. Rather than
analyzing a large number of individual gels using the DIA module, an
automated module, the Batch Processor, can be used. This automatically
runs a series of sequential DIA analyses with no need for user
intervention and can be set up to then automatically match the
individual spot maps, perform statistical analysis then generate a pick
list in the BVA module.
13.5.1 Setting up the batch processor
The first stage of the process is to create a DIA batch list by defining
which gel images are to be processed, and in what order. The Primary
Image in every case is the specific standard image for that gel, and the
secondary and tertiary images are the control and treated sample
images (the order is arbitrary).
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1
Double click on the DeCyder Differential Analysis Software Batch
Processor icon on the desktop to open the Batch Processor interface.
2
Select File:New Batch. In the dialog box that now appears browse to
locate the folder entitled Tutorial IV in the Tutorial data files folder.
3
Select the image Gel 01 Cy2 Standard in the left hand panel, then
click the Primary -> button. Repeat the procedure for the Gel 01 Cy3
Control and Gel 01 Cy5 Treated using the Secondary -> and Tertiary ->
buttons, respectively. Click OK.
4
In the Result Files section of the box that now appears, ensure that
the DIA and the XML file names end in the number one (by typing
“1” as indicated above), as this enables the subsequent files to be
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named automatically with an incrementing number. It is
recommended that both files are named “Gel 1”. Click OK.
Note:
If the name does not end in a “1”, then you will be given
the opportunity to name the DIA and XML filenames for
each batch item.
The DIA results are generated in two different formats:
• A DIA file, which contains the images and raw data information
for each pair analysis.
• An XML file, which contains the raw data file that is
subsequently exported to the BVA (Biological Variance Analysis)
module.
5
Enter the value 2500 for the estimated number of spots and ensure
that the Auotdetected Pick Reference markers check box is deselected
and the Include in BVA batch list check box is selected. Click OK.
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The filter parameters generated previously in DIA can now be
entered in the Exclude filter dialog box. Place a tick in each of the
boxes on the left-hand side of the different filter properties to
activate the filter, then enter the filter parameters generated in DIA
in their respective boxes. Click OK.
The second set of images associated with the second gel now have to be
entered in the batch processor.
7
In the dialog box which now appears enter the primary, secondary
and tertiary gel images associated with the second gel in an identical
manner to the first gel. Click OK. The remaining gel processing
parameters (such as spot detection and the exclude filter variables
entered for the first gel) is automatically applied to the second gel.
Continue loading all the images in this sequence until all the images
from all four analytical gels have been loaded.
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8
The preparative gel used for generating the pick list and subsequent
spot excision is conventionally loaded last. Load the “Pick.gel” as
a primary image then click OK.
9
Since the file name contains the text “pick”, the software prompts
the user to set this gel as the “pick”, i.e. to use this gel for generating
the co-ordinates for the pick list. Select Yes in this dialog box.
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10 When all the images have been loaded click Cancel. The batch
information is then entered into the DIA batch list and the user is
prompted to set the BVA workspace. Click Yes.
13.5.2 Assignment of spot map attributes
To generate statistical data for the proteins that are expressed
differentially between the control and treated groups, it is necessary to
identify the three different spot map groups (control, treated and
standard) within the software, and to assign every image to one of the
three groups. The group names have to be created so that the
appropriate images can be assigned to them.
1
The Spot Map Assignment dialog box, associated with the first spot
map, automatically appears after the previous step. The standard
spot maps will be automatically assigned to the standard group if
the text Standard or Std is in the image name. It is therefore always
important to include the name of the group in the image name.
Click OK to confirm that the first spot map is an internal spot map.
2
The Spot Map Assignments dialog box for the second spot map then
appears. The second spot map will be derived from either a control
or treated sample, denoted in the gel image file name (apparent in
the dialog box title bar).
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To assign a spot map to either a control or treated group, the two
group names must first be created. Click on the Add button in the
Experimental Design section of the dialog box and type the name
Control. Click OK. Click the Add button again then type the name
Treated, click OK.
4
Once the two experimental groups have been created the remaining
analytical spot maps can be assigned to their group, sequentially
using the Group pull down menu in the Spot Assignment dialog box
followed by clicking OK.The last spot map is the pick gel (item 13).
This gel does not belong to an experimental group and can
therefore be left Unassigned. Deselect the Analysis check box in the
Spot Map Function part of the dialog box. Click OK to complete the
spot map assignment process.
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13.5.3 Protein statistics
Immediately after setting the spot map attributes the Protein Statistics
dialog box appears automatically. The level of statistical significance in
protein differences between the two experimental groups is calculated
using the T-test.
In the box entitled Population 1, select Control, and select Treated in the
box entitled Population 2. Ensure that the Student's T-test and Average
Ratio check boxes are selected and the Independent tests option button
is selected. Click on OK.
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13.5.4 Protein Filter
Immediately after setting the protein statistics parameters the Protein
Filter dialog box automatically appears. The Protein Filter assigns a
Protein of Interest and/or Pick status (dependent on which Filter Action
check box is selected) to protein spots based on user defined criteria
such as Student's T-test value, average ratio or volume.
1
Select the Assign proteins of interest and Assign Pick status check boxes
in the Filter Action section of the dialog box.
For the purpose of this tutorial, select to assign protein spots that
are on 75% of spot maps (i.e. restricted to proteins present in ≥ 9
spot maps) with a T-test score less than 0.01 and with an average
ratio greater than or equal to 1.5 or less than or equal to -1.5 and
a pick gel volume of 1×105-1×108 the pick gel (setting the
parameters to those identical to the figure below). Click OK.
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13.5.5 Saving the Pick List
Immediately after setting the protein filter parameters the Set Pick List
File Name dialog box automatically appears.
1
Browse to select the folder in which to save the pick list (for
convenience it may be placed in the folder Tutorial IV).
2
Enter an appropriate name of the pick list file.
Using the Save as type pull-down menu the file extension for the
pick list can either be a text or XML format for use with either
Ettan Spot Picker or Ettan Spot Handling Workstation,
respectively. Click Save.
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13.5.6 Saving the batch workspace
1 To save the batch workspace, select File:Save As and enter a name
for the workspace. It is useful to enter a name that relates to the
experiment. Click Save.
2
Set the BVA filename by clicking on the button marked Set on the
side of the BVA filename box (top right of screen). Enter a filename
that relates to the experiment (e.g. E. coli control-treated). This
creates an empty file into which the BVA analysis output will
subsequently be entered.
The Batch Processor workspace is now completed and ready to be run
Note: All items in the Batch Processor workspace can be edited by
double clicking the relevant cell in the DIA and BVA batch list
or selecting the relevant dialog window button on the top of the
screen.
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13.5.7 Running the batch processor
Select Process:Run batch. A prompt appears to request automatic setting
of the Master (the spot map that all gels are matched with). Select Yes
and the gel with largest number of detected spots will be assigned as the
Master spot map. Click OK in the next dialog box, if you wish to start
processing immediately. Alternatively, a user defined delay can be
introduced, however the batch processor must be left open during the
delay period.
Note: The Master can also be set when assigning the spot map
attributes by selecting the Master check box for the desired
master spot map.
The following screen is displayed while the Batch Processor is working.
Upon completion of the batch run the DIA, XML, BVA and pick list
files are automatically generated in the requested folders. The
preparative gel and pick list can then be taken to the appropriate Ettan
spot picking instrument for automated spot excision.
However, it is recommended that the BVA workspace is reviewed prior
to generating the pick list. A workflow of using the Batch Processor to
assign proteins of interest only is therefore recommended. This involves
following the above tutorial but only selecting the Protein of Interest
check box (without selecting Pick status) in the Protein Filter dialog box,
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then avoiding generating a pick list by cancelling the Set Pick List
Filename dialog box. In this way the subsequent BVA file will have
protein differences highlighted as proteins of interest, which can be
manually reviewed then confirmed, before assigning the proteins of
interest with a Pick status (see chapter 12).
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Appendix A Recommended workflow for analysis of
multiple gels
The following workflows describe the various recommended steps for
processing experiments that employ an internal standard, including
data analysis and pick list generation.
The workflow is split into three separate components which represent
three phases of the analysis process:
• Flow diagram 1 describes the steps in preparing and processing the
gels in the Batch Processor.
• Flow diagram 2 describes the steps in reviewing the proteins of
interest in the BVA module.
• Flow diagram 3 describes the steps involved in processing a
preparative gel then generating a pick list.
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A.1
A.1 Flow Diagram 1
Continued on next page.
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A.2
A.2 Flow Diagram 2
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A.3 Flow Diagram 3
Continued on next page.
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A.3
Continued from last page:
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B.1
Appendix B Understanding the digital image
This Appendix deals with the acquisition and definition of the digital
image. It explains some of the more common terms used in association
with digital images and gives a brief insight into the GEL file format,
which is the Typhoon imaging systems default format.
B.1 Image acquisition
In order for any digital computer processing to be carried out on an
image, it must first be acquired and stored within the computer in a
suitable form. Image acquisition is a critical step and all primary data
should ideally be stored exactly as it is recorded by the imaging device
without significant data compression, as this may affect the accuracy of
the recording. It should also be of the highest quality required for image
resolution in the particular application. Acquisition can be achieved
using a variety of scanners or digital imagers. These are usually based
on PhotoMultipier Tubes (PMTs) or Charge-Coupled Devices (CCD).
A PMT is an electro-optic device that converts light energy into
electrical current and amplifies the current, whilst a CCD is a siliconbased integrated circuit consisting of a dense matrix of photodiodes
that operate by converting light energy in the form of photons into an
electric charge.
B.2 The digital image
The most practical way of storing an image as digital data is to divide
the image into a grid of very small regions called “picture elements,” or
“pixels”. In the computer this digital grid or “bitmap” represents the
image. Each pixel is identified by its position in the grid, as referenced
by its row (x) and column (y) number. Each pixel has a different color
or gray scale value and together they form a representation of the
image.
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B.3
The images below show the digitalization procedure:
Original image.
Image is
partitioned into a
two-dimensional
array of square
sections. Each
division will be
used to form a
pixel.
The scanning
device then
collects a single
value to
represent the
entire square,
which it then
transmits to the
computer.
After the
computer has
received values
for each section,
the entire image
can be
reconstructed
using the pixel
(x, y) values.
B.3 Formatting graphic files
Once an image has been acquired, it is converted to a particular file
format for storage. There are a number of widely used image formats,
such as TIFF, GIF etc. and knowing which format to use is a key issue.
Some formats are proprietary and only useful in the program used to
modify or acquire the image. Others are useful for exchange between
programs and computer platforms or for presentation in web pages.
Image files include a certain amount of technical information, which is
stored in an area called the image header or tag. The image header may
be of use in displaying the image (e.g. length and width in pixels),
identifying the image (e.g. name or source), or identifying the owner.
Scientific images should be acquired and archived using an information
preserving or non-destructive format. The compression algorithms
associated with some file formats actually distort some of the pixel
information when the file is saved, while others do not. Loss of data
may be unacceptable for use in quantitative analysis and as a
consequence, DeCyder Differential Analysis Software only supports
specific compression formats.
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• Tagged Image File Format (TIFF) images
TIFF, an extremely complex and flexible image format is used to
exchange files between platforms and software applications. The TIFF
file consists of a number of labels (tags) that describe certain properties
of the file (such as gray levels, color table, byte format, compression size
etc.). After the initial tags, comes the data, which may be interrupted by
more descriptive tags.
Although the TIFF file is an industry standard it has many variants.
DeCyder Differential Analysis Software has been validated for file
formats generated by Amersham Biosciences imaging devices
recommended for Etttan DIGE system applications. The 16-bit TIFF
16
format has 2 = 65536 levels of signal resolution and is the most
commonly used file format for images. The image below shows a
schematic diagram of a TIFF file:
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B.4
• Gel Image File Format
Images scanned on the Typhoon are stored as GEL files, which are a
variation of the 16 bit TIFF and are purely gray scale files with one bit
for each pixel. These images have very wide dynamic ranges; typically
images with 105 or 1-100,000 levels of signal resolution are possible.
The GEL format uses a square root algorithm to compress the possible
100,000 levels of an image into the 65536 levels available; this is
located in the TIFF private tag domain. The square root compression
also provides higher signal resolution at the low end where small
changes in signal are more critical. This is important when
discriminating between two small values. The TIFF file will assign the
same number to two values that differ by a small amount, whereas the
GEL file accurately represents the data. The recommended instrument
for scanning Ettan DIGE system gels for DeCyder Differential Analysis
Software analysis is the Typhoon Variable Mode Imager.
B.4 Elements of the digital image
• Pixel intensity value
Each of the pixels that represent a stored image has a pixel intensity
value, which describes how bright that pixel is. This intensity value
represents a measured physical property i.e. emitted light. This value is
the average for the whole area covered by the pixel. A pixel's address is
denoted by its row and column co-ordinates in the two dimensional
image.
• Pixel or bit depth
Pixel or bit depth (referred to as bit depth from now on) refers to the
amount of information allocated to each pixel in a graphic image.
Pixels have different bit depths, which determine how many grayscales
are available to the image. Most image file formats store grayscale
8
information in the header section of the file. An 8-bit image, 2 or 256
16
grayscale values, a 16-bit image 2 or 65 536 grayscale values.
Due to variations in how graphics programs and conversion algorithms
work, it is best to start off with as much bit depth as possible. Large bit
depth also increases the dynamic range available when collecting
images of, for example, gels with protein or nucleic acid bands that are
going to be quantified by pixel intensity of the bands. When working
with images that have high bit depths, remember that to actually see
this information on the computer, the display must also be set to a high
bit depth (thousands or millions of colors).
For black and white or binary images, pixels need only two bits of
information (black or white), and hence the bit depth is 4.
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Another type of image is the grayscale image, which is one in which the
only colors are shades of gray. The number of grayscales used can vary
but 16-bit systems have 65, 536 shades of gray, 0 being black and,
65 535 being white.
Table B-2. Bit depth and their corresponding number of different combinations
available.
Depth
1 bit
2 bit
4 bit
8 bit
12 bit
16 bit
Example
0
10
0110
01010011
011010011101
1011000100101101
Combinations
2
4
6
256
4096
65536
• Pixel frequency histogram
The frequency histogram refers to the frequency representation of
different shades of gray or color in the image. A frequency histogram
displays the number of pixels representing each grayscale or color
value. Frequency histograms most commonly represent grayscale
images and have many uses: the histogram may reveal an under-or
overexposed image (too many pixels with values close to 0, or too many
with values close to 255 respectively), and the histogram can be
manipulated to change the image: frequency values can be deleted, and
upper and lower thresholds can be set.
B.5 Image dimensions
• Resolution
Image resolution is often confused with pixel dimension and refers to
the number of pixels displayed per unit length of an image. Pixel
resolution is the fineness of the divisions into which the scanner
partitions the image. When the divisions are extremely small, the
scanner is said to have high resolution; when the divisions are course,
the scanner has low resolution. DeCyder Differential Analysis Software
requires images which have a pixel resolution of 100 µm. Anything less
does not contain the required amount of information, whilst anything
more adds no further advantage to the image analysis procedure.
Resolution determines the area occupied by the images in conjunction
with the pixel dimension and is a measurement of clarity, or detail. It
can also refer either to an image file or the device, such as a monitor,
used to display it. The relationship between number of pixels and area
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is commonly expressed by number of pixels per inch (ppi) the more
pixels per inch the better the resolution.
Output (print or display) resolution is more commonly expressed in
terms of dots per inch (dpi). The resolution of a scanned image is also
expressed in dpi where the number of dpi is equal to the number of ppi
i.e. an image scanned in at 300 dpi will give you an image resolution of
300 ppi. Image-file resolution and output resolution combine to
influence the apparent clarity of a digital image when it is viewed. The
display monitor used will also influence apparent image quality.
The amount of resolution is often ruled by practical considerations e.g.
the higher the dpi number the more information in the file, and the
greater the ability to enlarge a detail from that image. If the principal
life of an image is on screen e.g. an image for a web page, as opposed
to being printed out, and if details for enlargement are not required
from it, a resolution of 100 dpi will be sufficient. So, just as with image
type, resolution needs to be matched to the purpose of the scan. The
Table below shows the size of an uncompressed 1" x 1" image in
different types and resolutions:
Resolution (dpi)
400x400
300x300
200x200
100x100
2-bit Black and
white (Kb)
20
11
5
1
8-bit grayscale
(Kb)
158
89
39
9
24-bit color (Kb)
475
256
118
29
• Dynamic range
Dynamic range is the ability of an imaging system to quantitatively
detect very dim and very bright features within a single image and is
related to bit-depth. It is a measurement of the number of bits used to
represent each pixel in an image and hence determines the number of
colors or shades of gray (grayscale) that can be represented in a digital
image.
The dynamic range of a system is a function of the analogue-to-digital
converter, the purity of the illuminating light, colored filters, and any
system noise. It is measured on scale from 0.0 (perfect white) to 4.0
(perfect black), and the single number given for a particular imaging
system tells how much of that range the unit can distinguish.
Variations in dynamic range of a system impact the quality of the
digitized image more than simple resolution does. High-end imaging
systems are more sensitive to the range of colors in the spectrum and
can record minor differences between two almost identical colors.
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Several variables determine a system’s dynamic range: pixel depth
(number of bits per pixel per color), sensitivity of the image capture
device i.e. CCD / PMT, accuracy of the focusing optics, and precision
of the measurement of the black and white points.
B.6 Image quality
Visual image quality is the cumulative result of the scanning resolution,
the dynamic range of the scanned image and the scanning device or
technique used. Image quality is often expressed in terms of resolution,
but other factors also affect the quality of an image file. Images are
often stored at much higher quality than they are displayed on a
monitor because most printing devices are capable of a much higher
resolution than screen displays.
A key trade-off in defining an appropriate level of image quality is the
balancing of file size and resulting storage requirements with quality
needs. Since pixel dimensions and color depth of a graphic image are
directly proportional to the file size of the image, the higher the quality
of an image, the more storage space it will occupy. High quality images
also require more system resources e.g. higher bandwidth, networks,
increased memory requirements and increased time and cost of the
scanning process. Effective image compression provides a key to
maintaining quality while using less storage space and system
resources. However, it is highly recommended that images be archived
onto CD-ROM to preserve storage space, particularly if using a
network file server.
• Background & noise
Background is defined as undesired signal often resulting from
autofluorescence or light scatter from a matrix or sample support. It
can be minimized by the selection of appropriate matrix or sample
support e.g. low fluorescence glass. Noise is defined as the statistical
uncertainty inherent in a measurement, such as the standard deviation
associated with measured background counts, e.g. with 2–D gels noise
can be attributed to contaminants with fluorescent properties similar to
the specific fluor being used. By ensuring that only high quality reagents
are used and recommended procedures are followed noise can be
minimized. The sensitivity of the imaging system can be adjusted by
changing exposure times for CCD based systems or voltage settings for
PMT based systems. The specific signal can be optimized to give the
highest signal-to-noise ratio thus ensuring the maximum amount of
information is obtained from the image.
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C.1
Appendix C Spot processing algorithms
C.1 DIA normalization
The DIA normalization algorithm represents a series of steps that
allows spot quantitation to be expressed as a function of the primary
image, where an unchanged protein is represented as zero. Therefore
when using an experimental design employing an internal standard, all
the analysis gels are expressed as a function of the internal standard
allowing accurate quantitative inter-gel comparative analysis.
To clarify some of the procedures involved in the DIA normalization, a
step by step algorithm description is provided.
1
Calculate spot volumes.
Based on the result of the spot detection algorithm, the volumes of the
detected spots are calculated and compensated for the appropriate
background level. Background is subtracted on a spot-specific basis, by
th
excluding the lowest 10 percentile pixel value on the spot boundary.
2
Calculate spot ratios.
All the spot volume pairs are combined to create the set of ratio values
for the entire spot map. The ratios are calculated according to:
Ri = log10(V2i/V1i),
(i)
Where V1i is the volume of spot i in the left, or primary gel image and
V2i is the volume of spot i in the right, or secondary gel image. The
index i runs over all spots that are included in the analysis. Spots that
have status “excluded” are thus not part of the normalization. The
ratio Ri is also limited to the range [-6, 6] to avoid infinite ratios for
zero volumes.
3
Calculate data histogram.
The ratio values are then combined to generate a histogram, plotting
spot frequency against Ri (i.e. log volume ratio). The resolution of the
histogram is 0.02.
4
Optimize a model histogram curve.
A normal distribution is fitted to the main peak of the frequency
histogram. The tallest peak of the histogram C is used as the starting
center position of the model curve, and all histogram data extending
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C.1
from the tallest peak in both directions is included until the histogram
value reaches below 10% of the main peak height. At this point any
data outside is excluded from the model curve fitting procedure. This
procedure is illustrated in the figure below. The model curve parameters
are then optimized to the selected histogram data using a standard
Least Means Square gradient descent algorithm. When the
optimization is terminated, the center of the model curve is denoted C'.
5
Normalize the primary spot map
An assumption is made that the majority of the spots do not change,
hence the modal peak of the model histogram is normalized to occur at
zero on the log volume ratio axis.
For this purpose the spot volumes for the primary gel are multiplied 10
C' resulting in the mode volume for the primary and secondary being
equivalent. i.e. the spot volumes in the primary spot map are
normalized using the following procedure.
V1i' = V1i * 10 C'
(ii)
Where V1i' is the resulting normalized volume of spot in the left, or
primary gel image.
The actual calculated volume of the detected spot is however never
altered. This modification of the volume is local to the normalization
procedure and ensures that the model peak is shifted so that the
majority of spots expressed as unchanged (i.e. log volume ratio of 0).
The modified spot ratios are calculated in this step according to:
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Ri' = log10(V2i/V1i'),
(iii)
This parameter is termed standardized log abundance in the BVA
module and is used for the statistical analysis.
6
Re-calculate data histogram.
The modified ratio values are then combined to form a new histogram
of the same resolution as the previous one. The normalized model
histogram and data histogram are viewable in the histogram view of the
DIA module, represented by the blue and red lines, respectively.
7
Calculate data standard deviation.
The standard deviation of all the spot volume ratios according to (iii) is
calculated. This gives a rough indication of the spread of the data set.
Additional comments on spot normalization
For this normalization procedure to be accurate, the number of spots
needs to be quite large. It is recommended that more than 100 spots are
present in the normalization. However, the more valid spots that are
present the better. If the number of spots to normalize is lower than 50,
a model curve is not calculated. In this case the position of the tallest
histogram peak is used as C' for normalization in (ii).
When spots in the workspace have been manually excluded, the
workspace should be re-normalized based on the new set of included
spots. This becomes exceedingly important if a large number of spots
have been manually excluded.
In the DeCyder Differential Analysis Software-DIA environment, the
expression ratios are displayed differently in the table than in the
histogram. The histogram ratio representation Ri' is illustrated above,
while the expression ratio, E, presented in the table is calculated
according to:
E = 10 Ri' for (Ri' >= 0)
E = –1/10
Ri'
for (Ri' < 0)
(iv)
This results in a value less than –1 for under-expressed ratios and a
value larger than 1 for over-expressed ratios. Equal ratios are expressed
as a ratio of 1.
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C.2
C.2 BVA Normalization
If an internal standard is not used in the experimental approach
DeCyder Differential Analysis Software can normalize spot maps
facilitating the comparison of spot volumes on different gels. The
normalized volumes for a set of spots for a protein are then expressed
as a ratio of the weakest spot. This parameter is referred to as the
abundance.
This approach is not recommended, as inter-gel quantitative
comparisons are not as accurate as the quantitation using an internal
standard.
The steps involved in the BVA normalization process are described
below.
1
Calculate spot volumes.
Based on the result of the spot detection algorithm, the volumes of the
detected spots are calculated and compensated for the appropriate
background level. Background is subtracted on a spot specific basis, by
excluding the lowest 10th percentile pixel value on the spot boundary.
2
Calculate data histogram.
A histogram is plotted for spot frequency against log10 spot volume for
each spot map.
3
Calculate center of volume.
A Gaussian distribution is fitted to the main peak of the frequency
histogram. The model curve parameters are optimized to the histogram
data using a standard Least Means Square gradient descent algorithm.
When the optimization is terminated, the center of the model curve is
denoted as center of volume (CoV). The CoV therefore represents the
central tendency of the spot volume population (i.e. a “typical” spot
volume on specific spot map).
4
Calculate normalized spot volumes
The normalized spot volume is calculated for each spot on every gel.
Vnormg = Vg/CoVg
(i)
Where Vnormg is the normalized volume of a spot, Vg is the volume of
the same spot and CoVg is the center of volume for the spot map.
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5
Calculate normalized spot ratios
The ratio of the normalized spot volumes relative to the smallest
normalized spot volume for a group of matched spots (i.e. of the same
protein) is calculated, according to:
Rnorm = Vnormg / Vnormmin
(ii)
Where Rnorm is the normalized ratio, Vnormg is the normalized
volume for a specific spot and Vnormmin is the smallest normalized
volume associated with the spot.
This ratio allows some degree of comparison between spots of the same
protein. This parameter is referred to as the abundance in the BVA
module.
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D.1
Appendix D Experimental design and set up
examples
In this appendix there are examples of complex experimental designs
and statistical analyses. There are descriptions of both a paired testing
and Two-Way ANOVA, which include details of the experimental
design, setting up the statistical analyses and interpretation of example
proteins.
D.1 Example of a paired experiment
Experimental objective
An experiment is designed to investigate changes in protein expression
caused by a drug treatment after 24 hours in human subjects.
Experimental design
Blood samples are taken prior to drug treatment from five volunteers.
Blood protein was individually extracted and stored. The drug was then
administered to each individual, blood samples were taken 24 hours
later and protein was extracted. Ten aliquot samples derived from the
five individuals were taken and pooled to act as the internal standard.
The pooled standard was labelled with CyDye DIGE Fluor Cy2
minimal dye. Pre-treated and treated blood samples from each
volunteer were independently labelled with either CyDye DIGE Fluor
Cy3 or Cy5 minimal dye. Equivalent amounts of labelled standard,
volunteer 1 pre-treated, volunteer 1 post-treated were mixed and
subjected to 2–D gel electrophoresis. The samples from the other 4
volunteers were similarly subjected to 2–D gel electrophoresis on
separate gels (as described in the table overleaf – standards on
individual gel not shown).
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D.1
Assigning groups and individuals in DeCyder Differential Analysis Software
Spot data can be assigned into two experimental groups: pre- and postdrug treatment represented by 1 and 2, respectively.
The same 5 volunteers are present in both groups, therefore the data
can be paired (i.e. assignment of individual volunteers within groups).
The CyDye DIGE Fluor Cy2 minimal dye labelled internal standard
samples are omitted from the table below.
Number Gel
1
1
2
1
3
2
4
2
5
3
6
3
7
4
8
4
9
5
10
5
CyDye Individual Group
Cy3
1
1
Cy5
1
2
Cy3
2
2
Cy5
2
1
Cy3
3
1
Cy5
3
2
Cy3
4
2
Cy5
4
1
Cy3
5
1
Cy5
5
2
The above parameters can be designated either in the DeCyder
Differential Analysis Software Batch Processor or from DeCyder
Differential Analysis Software BVA module. The resultant spot map
table is illustrated.
The experiment assesses the difference in protein expression between
two experimental groups of paired data, therefore a paired Student's
T-test is applicable.
The Student's T-test and the Paired Tests check boxes must therefore be
selected in the statistics dialog box. The Average Ratio check box can
also be selected to calculate the magnitude of any protein expression
change between groups.
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Example proteins
Fig D-1. Statistical data displayed for protein 580.
The independent T-test p value indicates that there is no significant
difference in the mean expression of protein 580 from pre- to post-drug
treatment groups. However, when data pairing is accounted for, the
Paired T-test p value indicates that there is a significant change.
Protein 580 expression levels appear to consistently increase regardless
of the initial protein abundance.
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D.1
Fig D-2. Statistical data displayed for protein 1142.
The independent and paired T-test p values indicate that there is no
significant difference in expression of protein 1142 from pre- to postdrug treatment groups. The observed decrease in protein expression
between experimental groups is not due to a consistent abundance
change of this protein between individuals. The independent and paired
T-test p values indicate that there is no significant difference in
expression of protein 1142 from pre- to post-drug treatment groups
(p=0.066 and p=0.160, respectively). The observed decrease in protein
expression between experimental groups is not due to a consistent
abundance change between individuals.
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D.2 Example of a two condition experiment
Experimental objective
An experiment is designed to investigate changes in protein expression
in two E.coli bacterial strains incubated at 37 oC over a 90 minute time
period.
Experimental design
o
Bacterial cultures derived from each strain were incubated at 37 C.
Cell sample aliquots were taken immediately prior to incubation then
30, 60 and 90 minutes after commencing incubation.
The cell samples were lysed, labelled with CyDye DIGE Fluor minimal
dye, then equivalent amounts of protein were subjected to 2–D gel
electrophoresis (as described in the table on the following page –
standards on individual gel not shown). Triplicate gels were run and
analyzed to account for experimental variation. Each gel contained a
CyDye DIGE Fluor Cy2 minimal dye labelled internal pooled standard
with CyDye DIGE Fluor Cy3 and Cy5 minimal dye labelled test
samples.
Assigning groups and conditions
There are two conditions present in this experiment:
• Condition 1: two bacterial strains represented by 1 and 2.
• Condition 2: Four sampling time points: Pre-incubation 30, 60,
and 90 minutes incubations are represented by 1, 2, 3, and 4,
respectively.
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D.2
The table below shows the designation of groups, conditions and
individuals for the spot maps generated from the 12 experimental gels.
The Cy2 dye labelled internal standard samples are omitted from the
table below.
Item
Gel
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
1
2
3
4
5
6
4
5
6
7
8
9
7
8
9
10
11
12
10
11
12
CyDye DIGE Fluor
Strain
Time
minimal dye
(Condition 1) (Condition 2)
Cy3
1
1
Cy3
1
1
Cy3
1
1
Cy5
1
2
Cy5
1
2
Cy5
1
2
Cy3
1
3
Cy3
1
3
Cy3
1
3
Cy5
1
4
Cy5
1
4
Cy5
1
4
Cy3
2
1
Cy3
2
1
Cy3
2
1
Cy5
2
2
Cy5
2
2
Cy5
2
2
Cy3
2
3
Cy3
2
3
Cy3
2
3
Cy5
2
4
Cy5
2
4
Cy5
2
4
Group
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
8
The above parameters can be designated either in the DeCyder
Differential Analysis Software Batch Processor or from DeCyder
Differential Analysis Software BVA. The resultant Spot Map Table is
illustrated on the following page.
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D.2
The experiment assesses two conditions (strain and time), consequently
a Two-Way ANOVA analysis is applicable.
The Two-Way ANOVA check box must therefore be selected in the
Protein Statistic dialog box to perform the statistical test.
Examples of the statistical outcome for selected spots are illustrated on
the following pages.
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D.2
Example proteins
Fig D-3. Statistical outcome for protein 545.
The 2-ANOVA-Strain and 2-ANOVA-time values are not significant
for protein 545. Therefore there is no strain-to-strain or duration of
incubation effects on the expression of this protein.
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D.2
Fig D-4. Statistical outcome for protein 162.
The 2-ANOVA-Strain and 2-ANOVA-time values are statistically
significant for protein 162. Therefore there are strain specific changes
in expression of this protein. (i.e. one strain consistently has higher
expression of this protein). Furthermore, the expression of this protein
increases significantly in both strains over time. However, there is no
significant interaction between strain and time of incubation, since the
2-ANOVA-interact is not significant.
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D.2
Fig D-5. Statistical outcome for protein 721.
The 2-ANOVA-Strain and 2-ANOVA-time values are statistically
significant for protein 721. Therefore there are strain specific changes
in expression of this protein. The expression of this protein also
changes significantly in at least one timepoint. Moreover, there is a
significant interaction (2-ANOVA-Interaction<0.01) between strain
and time, whereby there is a decrease in expression of protein 721 over
time that is specific to one strain.
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E.1
Appendix E DeCyder Differential Analysis Software
keyboard shortcuts
E.1
DIA module keyboard shortcuts
Spot Controls
Alt + C
Alt + P
Alt + N
Alt + I
Alt + X
Alt + K
Alt + S
Alt + M
Workspace
Alt + F
Alt + E
Alt + V
Alt + R
Alt + H
File menu
Ctrl + N
Ctrl + O
Ctrl + S
Ctrl + P
Edit menu
Ctrl + T
Ctrl + C
View menu
F7
F8
F9
Ctrl + B
Ctrl + A
Ctrl + R
Help menu
Shift + F1
Confirm spot
Previous
Next
Include
Exclude
Pick
Spot ID
Comment
Open
Open
Open
Open
Open
File menu
Edit menu
View menu
Process menu
Help menu
Create new workspace
Open workspace
Save workspace
Show print dialog
Add Spot to Table
Copy
Zoom In
Zoom Out
Fit to window
Contrast - Brightness
View Area in 3-D
Rotate 3-D
Help What's This?
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267
E.2
E.2
BVA module keyboard shortcuts
Workspace
Alt + F
Alt + E
Alt + V
Alt + R
Alt + H
File menu
Ctrl + N
Ctrl + O
Ctrl + S
Ctrl + P
View menu
F7
F8
F9
Ctrl + B
Ctrl + A
Ctrl + R
Alt + Enter
Modes
Alt + S
Alt + M
Alt + P
Alt + A
Help menu
Shift + F1
Protein Related
functions
Alt + ↓
Alt + ↑
Alt + Page Down
Alt + Page Up
Alt + →
Alt + ←
Alt + X
Alt + C
Alt + D
Alt + K
Alt + I
Ctrl + Q
Alt +Q
268
Open File menu
Open Edit menu
Open View menu
Open Process menu
Open Help menu
Create new workspace
Open workspace
Save workspace
Print
Zoom In
Zoom Out
Fit to window
Contrast - Brightness
View Area in 3-D
Rotate 3-D
Properties
Spot Map Table
Match Table
Protein Table
Appearance Table
Help What's This?
Select next row in current table
Select previous row in current table
Scroll down current table
Scroll up current table
Select next Spot Map/protein
Select previous Spot Map/ protein
Set focus to Controls
Confirm/Unconfirm protein or match
Add/Break match
Pick protein
Protein of Interest
Primary image scroll down menu
Secondary image scroll down menu
DeCyder Differential Analysis User Manual 18-1173-16 Edition AA
E.3
E.3
Batch Processor keyboard shortcuts
File menu
Ctrl + N
Ctrl + O
Ctrl + S
Ctrl + P
Ctrl + B
Ctrl + M
Ctrl + D
Ctrl + T
Edit Menu
Ctrl + E
View Menu
Ctrl + I
Ctrl + V
Process Menu
Ctrl + R
New Batch
Open
Save
Print
Add DIA Batch Item
Remove DIA Batch Item
Add Spot Map(s)
Remove Spot Map(s)
Edit Item
DIA Batch List
BVA Batch List
Process
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E.3
270
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F.1
Appendix F Related products and documentation
F.1
Related products
Item
Product Code
DeCyder Differential Analysis Software pre- 18-1163-05
installed on a PC and a single user licence
DeCyder Differential Analysis Software
18-1156-17
including single user licence
DeCyder Differential Analysis Software
18-1150-45
additional user licence
Ettan Spot Picker
18-1145-28
Ettan Spotter
For product
information please
inquire with your local
Amersham
Biosciences sales
office.
Ettan Spot Handling Workstation
18-1164-05
Ettan Spot Handling Workstation - LWS for 18-1164-06
integration with Ettan LWS
Ettan Digester 100-120V
18-1152-59
Ettan Digester 220-240V
18-1142-68
Ettan MALDI-ToF Pro 120 V
18-1156-54
Ettan MALDI-ToF Pro 240 V
18-1156-53
2–D Electrophoresis, Principles and
80-6484-89
Methods
Ettan DIGE Training CD
18-1164-39
Typhoon Variable Mode Imager
For product
information please
inquire with your local
Amersham
Biosciences sales
office
Table continued on next page
DeCyder Differential Analysis User Manual 18-1173-16 Edition AA
271
F.1
Item
CyDye DIGE Fluor Cy2 minimal dye
(25 nmol)
CyDye DIGE Fluor Cy3 minimal dye
(25 nmol)
CyDye DIGE Fluor Cy5 minimal dye
(25 nmol)
CyDye DIGE Fluor Cy2 minimal dye
(10 nmol)
CyDye DIGE Fluor Cy3 minimal dye
(10 nmol)
CyDye DIGE Fluor Cy5 minimal dye
(10 nmol)
CyDye DIGE Fluor Labelling Kit for Scarce
Samples
CyDye DIGE Fluor Labelling Kit for Scarce
Samples plus Preparative Gel Labelling
PlusOne Urea
PlusOne CHAPS
PlusOne DTT 5 g
Ettan 2–D Quant kit
Ettan 2–D Clean-Up kit
PlusOne Bromophenol Blue
PlusOne Tris
PlusOne SDS powder
PlusOne Glycerol (87%)
PlusOne ReadySol IEF (40% T, 3% C)
PlusOne TEMED
PlusOne Ammonium Persulphate
PlusOne Glycine
Ultrapure DMF
DeStreak Rehydration Solution
272
Product Code
RPK 0272
RPK 0273
RPK 0275
25-8008-60
25-8008-61
25-8008-62
25-8009-83
25-8009-84
17-1319-01
17-1314-01
17-1318-02
80-6483-56
80-6484-51
17-1329-01
17-1321-01
17-1313-01
17-1325-01
17-1310-01
17-1312-01
17-1311-01
17-1323-01
US14862
17-6003-19
DeCyder Differential Analysis User Manual 18-1173-16 Edition AA
F.2
F.2
Related documentation
Document
Ettan DIGE system User Manual
Ettan DIGE Quick Protocols
Typhoon™ Instrument Guide
Ettan 2D-MS and LWS software Laboratory
Guide
Ettan 2D-MS and LWS software Laboratory
Guide Appendices
Ettan Spot Handling Workstation 2.01 LWS 1.0, User Manual
Ettan MALDI ToF Pro, User Manual
DeCyder Differential Analysis User Manual 18-1173-16 Edition AA
Code no.
18-1173-17
18-1164-41
63002831
18-1167-27
18-1170-99
18-1163-89
18-1144-01
273
F.2
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G.1
Appendix G Glossary
G.1 DeCyder Differential Analysis Software glossary
2SD value. 2 standard deviation of the spot ratio distribution, 95% of
the spots lie within this ratio for normally distributed data.
Abundance. The relative volume among all spots representing a
particular protein in a BVA data set. The weakest spot is taken as 1.00
and the others are displayed relative to this.
ANOVA. ANalysis Of VAriance is a family of methods used to perform
statistical analysis on experimental results.
ANOVA 1 way. One-Way ANOVA test (assigns statistical significance
to differences in standardized protein abundance between experimental
groups).
ANOVA 2 way. Two-Way ANOVA test, (assigns statistical significance
to both separate and mutual effects of two experimental conditions on
standardized protein abundance).
Appearance Table. Region within a BVA workspace where users can
track all information on a particular spot from all the gel images.
Area of Interest. User defined region outside of which any detected
protein spots are excluded from analysis.
Artifact Rejection. Filtering out of non proteinaceous signal.
Auto Level. The stage in the algorithm in which spots are matched.
Bandwidth. The transmission capacity of a communications channel,
usually expressed in bits or bytes per second.
Batch Processor. DeCyder Differential Analysis Software module
capable of performing fully automated co-detection, quantification and
matching of multiple spot maps.
BVA. Biological Variance Analysis module of DeCyder Differential
Analysis Software.
BVA Batch list. The spreadsheet in the batch processor depicting which
spot maps are to be matched.
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G.1
Centre of Volume (CoV). Central tendency of the spot volumes on a
spot map.
Co-detection. Simultaneous detection of labelled protein spots from
two in-gel images.
Comment. User defined text string that can be linked to specific protein
or spot map.
DeCyder Differential Analysis Software XML. XML format used to
transfer data between the various DeCyder Differential Analysis
Software modules.
DIA. Differential In-gel Analysis module of DeCyder Differential
Analysis Software capable of the fully automated co-detection,
quantification and matching of an in-gel image pair.
DIA Batch List. Spreadsheet in the batch processor depicting which
image pairs are to be detected and quantified.
Exclude Filter. Filter used to remove non proteinaceous artifacts such
as dust.
Groups. The collection of spot maps relating to image pairs whose
sample images have undergone exactly the same experimental
conditions (e.g. 3 spot maps of samples from disease patients treated
with drug A at a specific dose is a single group).
Histogram Selections. User adjustable parameters that alter the
appearance of the histogram in the DIA module.
Independent Data. Data sets which have no effect on one another.
Label. Indicates the fluor used to label the protein.
Landmarking. Process of manually matching spots to the master image
before automated matching to aid the matching algorithm.
Master Image. Spot map to which all others are matched to.
Match confirmation. Manual verification by the user of matched spots.
Matching. Automatic linking of spots on selected images to the
corresponding spot on the master image.
Match table. Area within a BVA workspace where spot match data is
displayed.
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G.1
Maximum Peak height. Pixel value at the X,Y position of the spot. This
is the actual detected peak height value compensated for background
level.
Maximum Volume. The highest volume from two co-detected spots.
Normalization. Process that allows the direct comparison of spot data
derived from different gels.
Null hypothesis. Posits that there is no difference between variables
being tested. To reject it is to infer “statistical significance”.
Paired Data. Data sets with an association between data point in every
group.
Pick Filter. Function that enables the user to designate spots for picking
based on user defined parametric values.
Primary image. Refers to the left hand gel image present in the image
view of the DIA module and the spot map table, protein table and
appearance table modes of the BVA module. In the Match Table mode
of BVA module, the primary image is the right hand image as the left
hand image is taken by the master gel.
Protein ID. Unique protein identifier that can be used to search.
Protein Table. Area within a BVA workspace where all the statistical
data from the analytical gels is located.
Protein Statistics. Function in the BVA module that applies statistical
tools to the protein data (providing that an experimental design with an
internal standard was used).
Repeated Measures. ANOVA Statistical ANOVA test applied to paired
data.
Scatter Parameter. The spot data type used to display the spot ratios in
the histogram. This is shown in the right y-axis on the histogram and
can be maximum slope, volume, peak height or area.
Secondary image. Refers to the right hand gel image present in the
image view in the DIA module and the spot map table, protein table
and appearance table mode of the BVA module.
Slope. Maximum gradient associated with the 3 dimensional attributes
of a spot map pair.
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G.1
Spot Controls. Allows user to control, assign or input data to the
workspace or individual protein spots.
Spot Map Table. Area within a BVA workspace where all the spot map
images and their associated assignments are located.
Spot Number. Unique identifier for a spot in DIA or a spot set in BVA.
Standard Image. Image relating to the internal standard from each gel.
Standardization. Process of quantifying spot map data in relation to the
corresponding standard spot map data.
Standardized Abundance. Abundance relative to the standard image
displayed as a ratio.
Student’s T-test. Statistical test that assesses differences between two
populations.
Threshold Mode. Value above or below which spots are classed as
being differentially expressed.
TIFF Image. Flexible image format used to exchange files between
platforms and software applications.
Volume. Sum of the detected pixel values above background within a
spot boundary.
Volume Ratio. Refers to the ratio of the normalized volumes of a pair
of spots from a spot map pair. A value of 2.0 represents a two-fold
increase while -2.0 represents a two-fold decrease, whilst a value of
1.00 represents an unchanged spot.
Workspace. Environment where all the experimental data is stored.
XML (Extended Markup Language). Structured universal tagged
language.
XML Toolbox. Shell, housing modules used for the extraction of data
from DeCyder Differential Analysis Software XML files in the form of
tab separated text or web tables.
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Index
Numerics
3-D View .................................................................. 28, 36, 56
A
Abundance ........................................................................... 255
Add attribute ......................................................................... 142
Add databases ........................................................................ 95
Addresses ................................................................................. 3
Analysis image ........................................................................ 58
Annotation ......................................................................55, 102
ANOVA .................................................................................... 81
one-way ............................................................ 75, 83, 98
two-way ............................................................. 75, 85, 98
two-way, example ....................................................86, 261
Appearance ............................................................................. 98
Appearance Table ................................................................. 101
Area of interest ........................................................................ 43
Artifact .................................................................................... 44
Assign picking references ...................................................... 108
Attributes .............................................................................. 142
Average ratio ........................................................................... 78
B
Background .......................................................................... 249
Batch list ............................................................................... 121
Batch processor ................................................. 121, 157, 226
BVA ...............................................................................51, 156
BVA Module ............................................................................ 51
C
Calculate Mw and pI ................................................................ 92
Center of volume ................................................................... 254
Co-detection ............................................................................ 31
Computer requirements ........................................................... 23
Conditions ............................................................................... 87
Consumables ........................................................................ 272
Contrast and Brightness .......................................................... 34
Create Workspace ............................................................29, 53
Customizing display colors ..............................................48, 103
CyDye DIGE Fluor minimal dyes ............................................... 12
D
Data Control Panel ................................................................... 28
Database linking ...................................................................... 94
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Database query in DeCyder BVA ........................................... 151
DeCyder XML ........................................................................ 129
DeCyder XML toolbox ............................................................ 140
LWS Pick List tool .......................................................... 146
Protein Identification tool ....................................... 140, 148
Detected proteins in gel ......................................................... 149
DIA .......................................................................27, 155, 159
DIA Graphical User Interface ................................................... 28
DIA Module ............................................................................. 27
DIA Processes ......................................................................... 27
Digital image ......................................................................... 243
Display Mw and pI values ........................................................ 93
Dust particles .......................................................................... 44
Dynamic range ...................................................................... 248
E
Enter Mw and pI ..................................................................... 91
Enter samples into Ettan LWS ................................................ 140
as a gel .......................................................................... 144
as a tube ....................................................................... 142
overview picture ............................................................. 141
Estimated Number of Spots ..................................................... 32
Ettan DALT electrophoresis unit ............................................... 32
Ettan DIGE system .................................................................. 12
Ettan Spot Handling Workstation ............................................. 27
Ettan Spot Picker .................................................................... 27
Evaluation file ......................................................144, 145, 148
Exclude Filter ................................................................. 44, 203
Exporting data .........................................................49, 50, 106
Extract data ........................................................................... 130
F
Frequency distribution ............................................................ 40
G
Gel artifact removal ............................................................... 224
Generate pick list .................................................................. 119
Generate standard report "Detected proteins in gel" ............... 149
Graph view .............................................................................. 71
Graphical user interface .......................................................... 51
Groups .................................................................................... 59
defining ........................................................................... 74
H
Histogram ............................................................................... 40
Histogram View ................................................................ 28, 40
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I
Image acquisition .................................................................. 243
Image file .....................................................................144, 148
Image View ............................................................... 28, 34, 54
Immobiline DryStrip ................................................................. 32
Import data ........................................................................... 114
Importing protein data templates into the BVA module ........... 151
Independent analyses .............................................................. 76
Installation ............................................................................... 23
Isoelectric point calculation ...................................................... 91
K
Keyboard shortcuts ................................................................ 267
L
Landmarking ........................................................................... 63
Load XML files ....................................................................... 130
M
Manual spot exclusion ............................................................. 46
Master image ...................................................................58, 63
Match confirmation ................................................................. 65
Match Table ............................................................................ 70
Matching ..........................................................................63– 65
Max peak height ...................................................................... 38
Max volume ............................................................................. 38
Model curve ............................................................................ 40
Molecular weight calculation .................................................... 91
Multiple images ..................................................................... 226
MW value .............................................................................. 148
N
NCBI ..................................................................................... 151
Noise .................................................................................... 249
Non-protein spots .................................................................... 44
Normalization
BVA ............................................................................... 254
DIA ................................................................................ 251
O
One-Way ANOVA ...................................................... 75, 83, 98
Open Workspace ..................................................................... 30
Original DeCyder BVA workspace ..................................148, 151
P
Paired analyses ....................................................................... 76
example ......................................................................... 257
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Paired data .................................................................... 76, 258
Patents and Licences ................................................................ 3
Peak height ............................................................................. 37
PI value ................................................................................ 149
Pick filter .............................................................................. 110
Picking image ......................................................................... 58
Picking reference ........................................................... 56, 108
Pixel ..................................................................................... 246
Post-translational modification .......................................... 48, 94
Preparative gel ...................................................................... 107
Primary image ......................................................................... 29
Print ....................................................................................... 49
Process multiple images ........................................................ 226
PROSITE ................................................................................. 95
Protein AC .............................................................................. 98
Protein confirmation ................................................................ 94
Protein data .......................................................................... 151
Protein Filter .................................................................. 90, 112
Protein ID ...................................................................... 98, 148
Protein Name ........................................................................ 148
Protein of interest .................................................................... 94
Protein Table .......................................................................... 98
PTM ................................................................................ 48, 94
Q
Quality score ........................................................................... 67
Quantitation ..................................................................... 33, 53
R
Reagents .............................................................................. 272
Recommended workflow ....................................................... 237
Flow Diagram 1 .............................................................. 238
Flow Diagram 2 .............................................................. 240
Flow Diagram 3 .............................................................. 241
Reference manual ................................................................... 11
Reference markers ................................................................ 108
Related documentation ......................................................... 273
Related products ................................................................... 271
Repeated measures ................................................................ 83
Resolution ............................................................................. 247
Run batch processor ............................................................. 235
S
Sample Attribute Templates .................................................. 142
Sample Definition
gel ................................................................................. 145
tube ............................................................................... 143
Save .............................................................................. 49, 104
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Scatter parameter .................................................................... 40
Scierra LWS ..................................................................143, 145
Scierra web ..................................................................142, 144
Secondary image ..................................................................... 36
Signature ................................................................................. 55
Slope ....................................................................................... 39
Spot area ................................................................................. 37
Spot confirmation .................................................................... 46
Spot detection ......................................................................... 31
single, double, triple ......................................................... 29
Spot exclusion ......................................................................... 43
Spot Map attribute ................................................................... 58
Spot Map function ................................................................... 58
Spot Map Table mode ............................................................. 58
Spot merging ........................................................................... 68
Spot number ........................................................................... 37
Spot position ........................................................................... 37
Spot ratio ................................................................................. 33
Spot statistics .......................................................................... 41
Spot volume .....................................................................33, 37
Standard report "Detected proteins in gel" .............................. 149
Statistics .................................................................................. 73
Student’s T-test ................................................................75, 78
SwissProt .........................................................................95, 97
T
Table View ........................................................................28, 38
parameters ....................................................................... 39
Tagged Image File Format (TIFF) ........................................... 245
Template Spot Map ................................................................. 58
Threshold ................................................................................ 41
Trademarks ............................................................................... 3
TrEMBL ................................................................................... 95
Tutorials .................................................................................. 11
Two-Way ANOVA ...................................................... 75, 85, 98
Two-Way ANOVA, example .................................................... 261
U
User manual ........................................................................... 11
User-defined protein labelling .................................................. 94
V
Volume .................................................................................... 33
W
Workspace
create .............................................................................. 29
open ................................................................................ 30
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properties ........................................................................ 31
X
XML ...................................................................................... 129
Tag definitions ............................................................... 132
XML Toolbox ......................................................................... 129
Z
Zoom ...................................................................................... 34
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TC information, Uppsala. Printed in Sweden by TK tryck AB, Uppsala.
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