rROMA identifies coexpressed gene sets
The rROMA package computes the significance of gene sets with coordinated expression in PCA space. Although its goal is similar to that of GSEA it uses a different approach to identify relevant gene sets.
- Tutorial on using rROMA on bulk RNASeq data
- R markdown to build the tutorial on rROMA
- Basic R script for using rROMA on bulk RNASeq data
Preparing for rROMA analysis
How to install rRoma, load all necessary packages and prepare your bulk RNASeq data for rROMA analysis.
Loading the gene sets
How does rROMA work?
Identification of shifted gene sets
Shifted gene sets are DE, most genes of the gene set are either up or down.
Identification of overdispersed gene sets
Overdispersed gene sets are DE but contain a mix of up and down genes.
Visualization of significant gene sets
Extra visualizations that allow to
- compare (groups of) samples in terms of shifted and overdispersed gene sets
- assess the reliability of the shifted and overdispersed gene sets
Are the rROMA results relevant?
Comparison with gProfiler results and checking literature to assess the relevance of the rROMA results.