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==
A quick jump into the most important topics for learning to use geWorkbench.
An introduction to the use of geWorkbench.
Many geWorkbench commands are available in the upper menu bar, as well as in the Workspace.
Customize geWorkbench to your needs. geWorkbench comes initially configured with only basic components installed. Use the CCM to load additional available modules.
The Workspace 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.
This component provides query access to a precomputed database of competitive endogenous RNA (ceRNA) interactions, also called "sponge" interactions. These interactions underlie a post-transcriptional layer of regulation, and were predicted using the Hermes algorithm (Sumazin et al., 2011).
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.
This component allows geWorkbench to run Consensus Clustering on a GenePattern server.
Cytoscape is used to display network interaction diagrams (from adjacency matrices). It features two-way interaction with the geWorkbench Markers component.
Cupid (Sumazin et al. 2011) generates information that can help predict if a gene is a target of a specific miRNA. The Cupid service provides a simple query interface to a database of precalculated Cupid results.
The DeMAND (Drug Mode of Action through Network Dysreguation) algorithm measures dysregulation between the expression of two genes in a network caused by e.g. a drug perturbation. The list of top dysregulated gene pairs can reveal details of a drug's mode of action in the tested cellular system or tissue.
Several variants of the t-test are available.
View and manipulate a histogram of the distribution of expression values for each array.
Compare the ratio of the expression of genes between two sets of arrays, e.g. case and control sets.
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.
GenomeSpace allows for the transfer of data between a number of different genomics and bioinformatics software analysis platforms, including geWorkbench.
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.
Implements a front-end for submitting data to and viewing the results of a GSEA (Subramanian et al, 2005) analysis on a GenePattern server.
geWorkbench implements its own agglomerative hierarchical clustering algorithm.
The IDEA (interactome dysregulation enrichment analysis) algorithm uses a genome-wide molecular interaction map as a systematic framework for the identification of genes playing a role in oncogenesis.
Jmol is a molecular structure viewer for viewing PDB format files.
Provides an interface to running K-Means Clustering on a GenePattern server, and a viewer for the results.
This component provides for query and display of data generated by the Columbia LINCS Technology U01 and Computation U01 Centers. It provides experimental and computational results for drug mode of action and similarity calculations, and for synergy experiments.
Marker annotations can be retrieved, including BioCarta pathway diagrams.
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.
Master Regulator analysis [Lefebvre et al., 2010] is an algorithm used to identify transcription factors whose targets (e.g., as represented in an ARACNe-generated interactome) are enriched for a particular gene signature (e.g. a list of differentially expressed genes).
Master Regulator Analysis using Fisher's Exact Test.
Master Regulator Analysis using the MARINa algoarithm. GSEA is used to compute enrichment.
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.
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.
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.
Interface to run the R implementation of Significance Analysis of Microarrays.
Genomic and protein sequences for selected genes can be retrieved for further analysis.
Search the SkyBase database with a sequence of interest to find homology models which meet user-defined alignment coverage and sequence identity constraints. SkyBase is a database that stores the homology models built by SkyLine analysis for
- structures in the RCSB Protein Data Bank (PDB) with a 60% redundancy cutoff
- (PDB60) structures in the Northeast Structural Genomics Consortium database
SlyLine is a pipeline for large-scale protein homology modeling. SkyBase provides access to precomputed models generated using SkyLine.
Clustering using Self-Organizing Maps.
Classification using Support Vector Machines.
The VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis) [Alvarez et al., manuscript in preparation] component in geWorkbench transforms the expression profile for each sample (column) into a transcription-factor activity profile, representing the relative activity of each TF in each sample.
The Volcano Plot graphically depicts the results of the t-test for differential expression. The log2 fold change for each significant marker is plotted against the -log10 of the P-value.
- This page was last modified on 23 January 2014, at 22:52.
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