geWorkbench

Revision as of 19:51, 21 December 2006

Home \| Quick Start \| Basics \| Menu Bar \| Preferences \| Component Configuration Manager \| Workspace \| Information Panel \| Local Data Files \| File Formats \| caArray \| Array Sets \| Marker Sets \| Microarray Dataset Viewers \| Filtering \| Normalization \| Tutorial Data \| geWorkbench-web Tutorials	Analysis Framework \| ANOVA \| ARACNe \| BLAST \| Cellular Networks KnowledgeBase \| CeRNA/Hermes Query \| Classification (KNN, WV) \| Color Mosaic \| Consensus Clustering \| Cytoscape \| Cupid \| DeMAND \| Expression Value Distribution \| Fold-Change \| Gene Ontology Term Analysis \| Gene Ontology Viewer \| GenomeSpace \| genSpace \| Grid Services \| GSEA \| Hierarchical Clustering \| IDEA \| Jmol \| K-Means Clustering \| LINCS Query \| Marker Annotations \| MarkUs \| Master Regulator Analysis \| (MRA-FET Method) \| (MRA-MARINa Method) \| MatrixREDUCE \| MINDy \| Pattern Discovery \| PCA \| Promoter Analysis \| Pudge \| SAM \| Sequence Retriever \| SkyBase \| SkyLine \| SOM \| SVM \| T-Test \| Viper Analysis \| Volcano Plot

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 (in preparation) and in the Online Help section of the program.

All data sets used in the tutorials are available from the download area of our site.

Getting Started

Obtaining and installing geWorkbench. Requirements.

Basics

A brief introduction to the use of geWorkbench.

Projects and Data Files

Creating projects, loading microarray data files, merging into one dataset, and saving.

Project Details

Creating projects, loading microarray data files, merging into one dataset, and saving.

Data Subsets

Subsets of both markers and arrays can be defined for targeted analysis.

Viewing a Microarray Dataset

Survey of geWorkbench visusaliztion tools for microarray data.

Filtering and Normalizing

geWorkbench provides numerous methods for filtering and normalizing microarray data.

Expression Value Distribution

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

Differential Expression

Several variants of the t-test are available.

Clustering

Data can be clustered using a fast hierarchical clustering routine, as well as SOMs.

Marker Annotations

Marker annotations can be retrieved, including BioCarta pathway diagrams.

Sequence Retrieval

Genomic sequences for markers can be retrieved for further analysis.

BLAST

geWorkbench can run BLAST jobs on the JCSB cluster.

Pattern Discovery

Upstream seqeunce can be analyzed for conserved sequence patterns.

Promoter Analysis

Search a set of sequences against a promoter database.

Reverse Engineering

Simple network reverse engineering - microarray datasets can be analyzed for interactions between genes.

ARACNE

Formal method for reverse Engineering - microarray datasets can be analyzed for interactions between genes.

Network Browser

Provides visualization of adjacency matrix generated by ARACNE, using Cytoscape.

GO Term Enrichment

Determine if particluar Gene Ontology terms are overrepresented in a data subset.

Synteny

Compare genomic sequence from two different species.

Jmol

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

@@ Line 29: / Line 29: @@
 ===[[Tutorial - Filtering and Normalizing | Filtering and Normalizing]]===
 geWorkbench provides numerous methods for filtering and normalizing microarray data.
+===[[Tutorial - EVD | Expression Value Distribution]]===
+View and manipulate a histogram of the distribution of expression values for each array.
 ===[[Tutorial - Differential Expression | Differential Expression]]===