pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks

Abstract

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

Biological and Clinical Changes in a Pediatric Series Treated with Off-Label JAK Inhibitors

“…To investigate the involvement of potential predominant pathways selected differentially expressed genes were analyzed for pathway enrichment by running the R package pathfindR ( p -value threshold < 0.05) [ 38 ], according to the Reactome database. The output describes: The fold enrichment value calculated considering the list of enriched genes in a specific pathway and the number of total input genes.…”

CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer*

“…DFI was similar to PFI with the inclusion of censored patients with new primary tumor in other organ; patients who were dead with tumor without new tumor event and patients with stage IV were excluded. In DSS, disease-specific survival time in days, last contact days or death days, whichever was larger was used to identify dead vs censored patients [48]. To compare the drug responses between sample-cell line pairs, we compiled our ground truth drug response data for patients from TCGA database using OS and PFI as clinical end points.…”

“…We used biological pathways listed in KEGG [54] database for this analysis. For each cell line, we used frequently mutated breast cancer driver genes (listed in COSMIC) that were present in cell line's DE genes and inferred KEGG pathways with significant enrichment (FDR corrected p-value < 0.01 ) in this gene list using R package Pathfinder [48]. Similarly, for each sample, we used breast cancer driver genes that were present in sample's DE genes and inferred KEGG pathways with significant enrichment.…”

Transcriptomic Changes in Endothelial Cells Triggered by Na,K-ATPase Inhibition: A Search for Upstream Na+i/K+i Sensitive Genes

“…Active subnetwork search and enrichment analysis was provided by the pathfindR package in R [61]. Biogrid and KEGG sets were used for the identification of protein-protein interaction network (PIN) and necessary gene sets to obtain for enrichment analysis, respectively.…”