Hierarchical Clustering display of a K-means generated cluster. Samples are color-coded by disease state and cancer subtype.MeV's strength lies in its easy user-interface coupled with a powerful suite of statistical tools.

• Load a variety of data types, such as expression, SNP, exon, PPI and copy number data
• Test for differential expression, template matching, and functional enrichment of groups of features.
• Group and label features with color-coded tags and track those features through different analyses.
• Automatically download annotation data for arrays made by many manufacturers, such as Affymetrix, Illumina and Agilent.

Popular Modules

KNNC - K-Nearest Neighbors Classification (references)

KNN Classification is a supervised classification scheme. A subset of the entire data set (called the training set), for which the user specifies class assignments, is used as input to classify the remaining members of the data set. The user specifies the number of expected classes, and the training set should contain examples of each class.

SAM - Significance Analysis of Microarrays (references)

The SAM module is an implementation of the Tusher et al, 2001 paper describing the method of determining significance of gene expression changes between samples. SAM is useful when there is an a-priori hypothesis that some genes will have significantly different mean expression levels between different sets of samples.

For example, one could look at differential gene expression between tissue types, or differential response to exposure to a perturbation between groups of test subjects. A valuable feature of SAM is that it gives estimates of the False Discovery Rate (FDR), which is the proportion of genes likely to have been identified by chance as being significant. SAM is found under the Statistics menu in the MeV toolbar.

GSEA - Gene Set Enrichment Analysis (references)

GSEA is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). The GSEA module is found under the Miscellaneous menu in the MeV toolbar. Complete instructions are in the tutorial.

NMF produces a consensus matrix to display the robustness of its result cluster membership.NMF - Non-negative Matrix Factorization (references)

Non-negative Matrix Factorization is a technique which makes use of an algorithm based on decomposition by parts of an extensive data matrix into a small number of relevant metagenes.  NMF’s ability to identify expression patterns and make class discoveries has been shown to able to have greater robustness over popular clustering techniques such as HCL and SOM. 

Complete Module Listing

Clustering

HCL - Hierarchical clustering

TEASE - Tree EASE

ST - Support trees (Bootstrapping HCL)

SOTA - Self-organizing trees

KMC - K-Means Clustering

KMS - KMC Support (Bootstrapping KMC)

CAST - Clustering Affinity Search Technique

FOM - Figures of Merit

QTC - QT_Clust

SOM - Self-organizing maps

Statistics

PTM - Template matching

TTEST - T-Tests

BRIDGE -

SAM - Significance Analysis of Microarrays

ANOVA - One-way Analysis of Variance

2ANOVA - Two-way Analysis of Variance

NonpaR - Nonparametric Tests

BETR - Bayesian Estimation of Temporal Regulation

RP - Rank Products

Classification

SVM - Support Vector machines

USC - Uncorrelated Shrunken Centroids

KNNC - K-Nearest Neighbors Classification

DAM - Discriminant Analysis Classifier

Data Reduction

RN - Relevance networks

PCA - Principal components analysis

COA - Correspondence Analysis

TRN - Expression Terrain Map

Meta Analysis

GSEA - Gene Set Enrichment Analysis

EASE - Expression Analysis Systematic Explorer

Visualization

LEM - Linear Expression Map

GDM - Gene Distance Matrix

Miscellaneous

GSH - Gene shaving

BN - Bayesian Networks

LM - Literature Mining