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Revision as of 16:17, 14 February 2006

Overview

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geWorkbench is an open-source bioinformatics platform that offers a comprehensive and extendible collection of tools for the management, analysis, visualization and annotation of biomedical data.

Benefits include:

• Integration with existing bioinformatics modules for analysis and visualization.
• Support for a variety of genomic data including microarrays, sequences, pathways, networks, alignments and phenotypes.
• Access to remote servers and clusters for the performance of computationally intensive calculations.
• Accesses analyses with biological annotations from the National Cancer Institute.
• Flexible import options: Allows user to merge files from various sources.
• Community: decribe this aspect
• Insert developer benefit ( plugin)

Tutorials

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Getting Started

• Starting the application
• GUI elements
  • Panels
  • Navigation

Loading Data

• Data formats

Working with Marker and Phenotype Panels

Creating panels

We can now assign phenotypes to each chip. We will place the phenotypes in the default group, however you can create new phenotype groups by pushing the New button on the Phenotype Panel at lower left.

Here we select and label arrays in the Phenotype Panel which contain samples from the congestive cardiomyopathy disease state...

Next, we can similarly label the remaining arrays as "Normal". We have also checked boxes to indicate that these groups of arrays are "Active". Various analysis and visualization components can be set to only use/display activated arrays or markers.

For statistical tests such as the t-test the Case and Control groups can be specified. This is done by left-clicking on the thumb-tack icon in front of the phenotype name. Here we are specifying the disease arrays as the "Case". The remaining "Normal" arrays are by default labeled control.

A red thumbtack indicates the arrays have been specified as "Case".

Visualize Panels

Here we select the relative display type.

Returning to the Open File dialog as we before by right-clicking on the project entry, we will select the "cardiomyopathy.exp" file we previously saved...

Resulting in the following colorful display of the array data for the first array....

Visualize Gene Expression

Visualization tools provide a view of the chip(s) under investigation and can be used for ascertaining the quality of the data. The phenotype and gene panel can be used to limit display. The images can be saved and exported.

The Microarray View can be used to inspect each separate microarray using the scroll bar.

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The Tabular Microarray Panel can be used to see data in spreadsheet format. One row is created per individual marker/probe and one column per microarray.

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Color Mosaic Heat maps for microarray expression data, organized by phenotypic or gene groupings.

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Expression Profiles This is a line graph of genes expression profiles across several arrays/ hybridizations. (insert image)

Expression Value Distribution (EVD) A distribution plot of marker expression values across one or more microarrays.

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Scatter Plot A pairwise (array vs. array and marker vs. marker) comparison and plotting of expression values. (insert image)

Filter and Normalize Data

Normalize

Normalization can be used to decrease the effects of systematic differences across a set of experiments. In caWorkbench, normalization results in replacing values with new values.

Available geWorkbench normalization methods:

• log2 Transformation
• Threshold Normalizer
• Array-based Centering
• Missing Value Computation
• Mean–variance Normalizer
• Marker-based Cntering

Normalizer	Description	Normalization Tool Description Missing value calculation Replaces every missing value with either the mean value of that marker across all microarrays or with the mean measurement of all markers in the microarray where the missing value is observed. Log2 Transformation Applies a log2 transformation to all measurements in a microarray. Threshold Normalizer All data points whose value is less than (or greater than) a user-specified minimum (maximum) value are raised (reduced) to that minimum (maximum) value Marker-based centering Subtracts the mean (median) measurement of a marker profile from every measurement in the profile Array-based centering Subtracts the mean (median) measurement of a microarray from every measurement in that microarray. Mean-variance normalizer For every marker profile, the mean measurement of the entire profile is subtracted from each measurement in the profile and the resulting value is divided by the standard deviation. Filter Filtering can be used to screen out missing data points, remove low quality data or reduce the size of the dataset by removing less interesting data. Available geWorkbench filter methods: Missing Values Deviation Expression Threshold Affy Detection 2 Channel Threshold Clustering Gene Expression Data Hierarchical Clustering Self Organizing Map (SOM) Differential Expression T Test Multi Test Volcano Plot Color Mosaic Regulatory Network Reverse Engineering Cytoscape Integrated Annotation Information Enrichment Analysis Go Term Go Miner Sequence Analysis Sequence Retrieval Sequence Homology Analysis Blast Other Pattern Discovery Position Histogram Promoter Analysis