Revision as of 15:23, 23 January 2014

The tutorials shown on this page provide a quick introduction to the most important features of geWorkbench. Additional information can be found in the User Guide and in the Online Help section of the program.

Using the basic framework of geWorkbench

The graphical interface, files and data==

Quick Start

A quick jump into the most important topics for learning to use geWorkbench.

Basics

An introduction to the use of geWorkbench.

Menu_Bar

Many geWorkbench commands are available in the upper menu bar, as well as in the Workspace.

Component Configuration Manager

Customize geWorkbench to your needs. geWorkbench comes initially configured with only basic components installed. Use the CCM to load additional available modules.

Workspace

The Workspace is where data is loaded and analysis results are stored.

Information Panel

Describes components use to record details of calculations and datasets.

Local Data Files

Covers loading data from files on your local computer.

File Formats

Details of several different file formats supported by geWorkbench.

caArray

How to download microarray data from caArray. geWorkbench can download "derived" data sets from caArray.

Array Sets

How to create and use sets of arrays for controlling data analysis.

Marker Sets

How to create and use sets of markers for controlling data analysis.

Viewing a Microarray Dataset

Survey of geWorkbench visualiztion tools for microarray data. Includes:

• Microarray Viewer
• Tabular Microarray Viewer
• CEL file image viewer
• Color Mosaic
• Expression Profiles
• Scatter Plot

Filtering

geWorkbench provides numerous methods for filtering microarray data.

Normalization

geWorkbench provides numerous methods for normalizing microarray data.

Tutorial Data

Downloadable data used in the tutorials.

Individual analysis and visualization components

Analysis Framework

Most analysis routines are located in the command area located in the lower right quadrant of geWorkbench. This section describes a common framework for saving parameter settings that these components share.

ANOVA

How to set up and run Analysis of Variance.

ARACNE

Formal method for reverse Engineering - microarray datasets can be analyzed for interactions between genes. Now includes new ARACNe2, which implements the much faster Adaptive Partitioning algorithm and accurate parameter estimation.

BLAST

Submits BLAST jobs to the NCBI server and displays and allows further interaction with alignment results.

Cellular Networks KnowledgeBase

The CNKB component queries a database of protein-protein and protein-DNA interactions maintained at Columbia University.

Classification

Several classification components have been ported by the GenePattern development team to work with geWorkbench. These include K-nearest neighbors (KNN), Principle Component Analysis (PCA), Support Vector Machines (SVM) and Weighted Voting (WV).

Color Mosaic

Displays expression results as a heat map.

Consensus Clustering

Cytoscape

Cytoscape is used to display network interaction diagrams (from adjacency matrices). It features two-way interaction with the geWorkbench Markers component.

Differential Expression (t-test)

Several variants of the t-test are available.

Expression Value Distribution

View and manipulate a histogram of the distribution of expression values for each array.

Gene Ontology Term Analysis

Finds Gene Ontology terms that are over-represented in a list of genes of interest.

Gene Ontology Viewer

The Gene Ontology Viewer provides both a standalone GO Term browser, as well as displaying results of GO Term Analysis. Genes associated with a term can be copied back into a marker set for further analysis.

genSpace

GenSpace is a social networking tool which allows patterns of use (putative workflows) of geWorkbench components to be inferred and queried. If desired, (participation is entirely optional) it can be used to identify potential expert users of particular components who may be able provide advice.

Grid Services

A number of geWorkbench data analysis components have been implemented as services on the National Cancer Institute's caGrid. caGrid is an infrastructure component of the NCI's caBIG(R) program.

Hierarchical Clustering

geWorkbench implements its own agglomerative hierarchical clustering algorithm.

Jmol

Jmol is a molecular structure viewer for viewing PDB format files.

MarkUs

The MarkUs component assists in the assessment of the biochemical function for a given protein structure. The component in geWorkbench provides an interface to the MarkUs web server at Columbia. MarkUs identifies related protein structures and sequences, detects protein cavities, and calculates the surface electrostatic potentials and amino acid conservation profile.

Marker Annotations

Marker annotations can be retrieved, including BioCarta pathway diagrams.

Master Regulator Analysis

The Master Regulator Analysis (MRA) component attempts to identify transcription factors which control the regulation of a set of differentially expressed target genes (TGs). Differential expression is determined using a t-test on microarray gene expression profiles from 2 cellular phenotypes, e.g. experimental and control.

MatrixREDUCE

MatrixREDUCE is a tool for inferring the binding specificity and nuclear concentration of transcription factors from microarray data.

MINDy

MINDy identifies modulators of gene regulation using conditional ARACNe calculations.

Pattern Discovery

Upstream seqeunce can be analyzed for conserved sequence patterns.

Principle Component Analysis (PCA)

Find components of the data responsible for the greatest variance. Provides a front-end to analysis on a GenePattern server, and graphical visualization of the results.

Promoter Analysis

Search a set of sequences against a promoter database.

Pudge

Pudge provides an interface to a protein structure prediction server (Honig lab) which integrates tools used at different stages of the structural prediction process.

SAM

Significance Analysis of Microarrays

Sequence Retriever

Genomic and protein sequences for selected genes can be retrieved for further analysis.

SOM

Clustering using Self-Organizing Maps.

SVM

Classification using Support Vector Machines.