I would like to introduce our group's active-subnetwork-oriented enrichment R package: pathfindR (latest release is v1.4.0).
This tool is designed to improve enrichment analysis by firstly identifying active subnetworks in differential expression/methylation data using a protein-protein interaction network. It then performs enrichment analysis (Over-Representation Analysis). By utilizing the interaction information, pathfindR identifies most of the key involved processes.
- pathfindR provides the human gene sets KEGG, Reactome, BioCarta, GO-MF, GO-BP, GO-CC and GO-All (BP+MF+CC) and the M.musculus KEGG mmu_KEGG gene set for enrichment analysis. The user can also use any other custom gene sets.
- pathfindR also creates pathway diagrams for hsa-KEGG with the involved genes colored by change values or visualizations of interactions of genes involved in the pathways and interaction graphs for other gene sets, displaying the interactions of involved genes
- The package allows for clustering of enriched terms and establishment of representative terms. This allows for further abstraction and reduces the complexity of analysis. The available clustering approaches are hierarchical and fuzzy.
- pathfindR provides functionality to score terms for individual samples and plot a heat map of the scores. This allows the user to investigate how a gene set is altered in a given sample (or in cases vs. controls).
- the package also offers a term-gene interaction graph visualizing terms and term-related genes as a graph to determine the degree of overlap between the enriched terms by identifying shared and/or distinct significant genes
You can install the released version of pathfindR from CRANvia:
And the development version from GitHub via:
install.packages("devtools") # if you have not installed this devtools::install_github("egeulgen/pathfindR")
You can read more about the method, its example applications on 3 different datasets and comparison with other available methods in our 2019 article.