Feature Set Enrichment Analysis for Metabolomics and Transcriptomics
Check an object of class tmodGS
Calculate the eigengene of a module from a data set
Create an evidence plot for a module
Filter by genes belonging to a gene set from a data frame
Get genes belonging to a gene set
Return the contents of a gene set
Create an evidence plot for a module (ggplot2 version)
Create a tmod panel plot using ggplot
Create a visualisation of enrichment
Convert a data frame to a tmod object
Plot a correlation heatmap for modules
Module correlation
Find group of modules
Jaccard index for modules
Calculate overlaps of the modules
Plot a PCA object returned by prcomp
Create an effect size / p-value plot
A combined beeswarm / boxplot
Simple Pie Chart
Default gene expression module data
Transcriptional Module Analysis
Query and set IDs of gene sets in a tmodGS object
Convert a tmod module set into a data frame
Convert the old tmod objects to the tmodGS objects
Calculate AUC
Count the Up- or Down-regulated genes per module
S3 class for tmod gene set collections
Import data from MSigDB
Leading Edge Analysis
Summary stats of a leading edge analysis
Up- and down-regulated genes in modules based on limma object
Run tmod enrichment tests directly on a limma object
tmod's replacement for the limma topTable function
A selection of color palettes
Plot a summary of multiple tmod analyses
PCA plot annotated with tmod
Create a summary of multiple tmod analyses
Tag cloud based on tmod results
Perform a statistical test of module expression
Upset plot
Methods and feature set definitions for feature or gene set enrichment analysis in transcriptional and metabolic profiling data. Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) <doi:10.1093/bioinformatics/btz447>.
Useful links