PathfindR: Enrichment Analysis for Omics Data
Ege Ulgen
The active-subnetwork-oriented enrichment analysis approach of pathfindR involves mapping input genes onto a protein interaction network, identifying active subnetworks based on their scores and significant gene content, and then performing enrichment analyses to identify significantly enriched terms. The results are reported in a data frame and an HTML report with visualizations.
Example use case: Analyzing gene expression data to identify enriched pathways or gene sets associated with differentially expressed genes
Technology: A software tool for enrichment analysis of gene expression data. Input data should be in .csv, .txt, or .tsv format, and should include 3 columns only: (i) gene names, symbols or pseudonyms, (ii) log fold change (logFC), and (iii) p-values, in this order. P-values should indicate the chance of getting data with this distribution if no real difference exists (i.e. such that by sheer chance a pattern is observed, not because there is a real pattern). Small p-values are usually desirable, especially if the goal is to identify significant differences in gene expression, with 'small' typically considered to be 0.05 or less.
Limitations:
- Reliance on protein interaction network data for mapping input genes.
Example use case: Analyzing gene expression data to identify enriched pathways or gene sets associated with differentially expressed genes
Technology: A software tool for enrichment analysis of gene expression data. Input data should be in .csv, .txt, or .tsv format, and should include 3 columns only: (i) gene names, symbols or pseudonyms, (ii) log fold change (logFC), and (iii) p-values, in this order. P-values should indicate the chance of getting data with this distribution if no real difference exists (i.e. such that by sheer chance a pattern is observed, not because there is a real pattern). Small p-values are usually desirable, especially if the goal is to identify significant differences in gene expression, with 'small' typically considered to be 0.05 or less.
Limitations:
- Reliance on protein interaction network data for mapping input genes.
- Need for appropriate thresholding of adjusted p-values for enrichment analysis.
- Potential for false positives or false negatives in the identification of active subnetworks and enriched terms.
- May not be suitable for datasets with small sample sizes or low statistical power.
Citation:
Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. https://doi.org/10.3389/fgene.2019.00858
Released: Jun-27-2022
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