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: Projects, files and data
A quick jump into the most important topics for learning to use geWorkbench.
An introduction to the use of geWorkbench.
Customize geWorkbench to your needs. geWorkbench comes initially configured with only basic components installed. Use the CCM to load additional available modules.
The Project Folders component is where data is loaded and analysis results are stored.
Describes components use to record details of calculations and datasets.
Covers loading data from files on your local computer.
Details of several different file formats supported by geWorkbench.
How to download microarray data from caArray. geWorkbench can download "derived" data sets from caArray.
How to create and use sets of arrays for controlling data analysis.
How to create and use sets of markers for controlling data analysis.
Survey of geWorkbench visualiztion tools for microarray data. Includes:
- Microarray Viewer
- Tabular Microarray Viewer
- CEL file image viewer
- Color Mosaic
- Expression Profiles
- Scatter Plot
geWorkbench provides numerous methods for filtering microarray data.
geWorkbench provides numerous methods for normalizing microarray data.
Downloadable data used in the tutorials.
Individual analysis and visualization components
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.
How to set up and run Analysis of Variance.
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.
Submits BLAST jobs to the NCBI server and displays and allows further interaction with alignment results.
The CNKB component queries a database of protein-protein and protein-DNA interactions maintained at Columbia University.
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).
Displays expression results as a heat map.
Cytoscape is used to display network interaction diagrams (from adjacency matrices). It features two-way interaction with the geWorkbench Markers component.
Several variants of the t-test are available.
View and manipulate a histogram of the distribution of expression values for each array.
Finds Gene Ontology terms that are over-represented in a list of genes of interest.
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 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.
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.
geWorkbench implements its own agglomerative hierarchical clustering algorithm.
Jmol is a molecular structure viewer for viewing PDB format files.
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 can be retrieved, including BioCarta pathway diagrams.
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 is a tool for inferring the binding specificity and nuclear concentration of transcription factors from microarray data.
MINDy identifies modulators of gene regulation using conditional ARACNe calculations.
Upstream seqeunce can be analyzed for conserved sequence patterns.
Search a set of sequences against a promoter database.
Pudge provides an interface to a protein structure prediction server (Honig lab) which integrates tools used at different stages of the structural prediction process.
Significance Analysis of Microarrays
Genomic and protein sequences for selected genes can be retrieved for further analysis.
Clustering using Self-Organizing Maps.
Classification using Support Vector Machines.
Tutorials for a number of components are under development, including:
Gene Pattern components:
- PCA (GenePattern) - Analysis and Viewer