mvGST provides platform-independent tools to identify GO terms (gene sets) that are differentially active (up or down) in multiple contrasts of interest. Given a matrix of one-sided p-values (rows for genes, columns for contrasts), mvGST uses meta-analytic methods to combine p-values for all genes annotated to each gene set, and then classify each gene set as being significantly more active (1), less active (-1), or not significantly differentially active (0) in each contrast of interest. With multiple contrasts of interest, each gene set is assigned to a profile (across contrasts) of differential activity. Tools are also provided for visualizing (in a GO graph) the gene sets classified to a given profile.

Versions 1.10.0, 1.12.0
License GPL-3


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-mvgst

and update with:

conda update bioconductor-mvgst


A Docker container is available at