LRpath

LRpath performs gene set enrichment (GSE) testing on RNA-sequencing (RNA-seq) data to identify over- and under-represented biological functions while adjusting for expression- and transcript-related biases.

Key Features:

• RNA-Enrich: Empirically adjusts for average read count per gene to mitigate biases associated with longer and highly-expressed transcripts.
• Logistic regression framework: Applies logistic regression to continuous gene-level statistics without requiring a P-value cutoff, preserving information for downstream GSE analysis.
• Bias correction: Improves type I error rate and detection power in gene set enrichment testing by accounting for average read count per gene.
• Flexibility: Operates without a P-value threshold, accommodating various experimental designs and small sample-sized RNA-seq experiments.
• Annotation support: Supports 16 different gene annotation databases for enrichment analysis.

Scientific Applications:

• GSE analysis of RNA-seq differential expression: Identification of over- or under-represented biological functions from RNA-seq differential expression results.
• Small-sample RNA-seq studies: Enrichment testing in experiments with limited sample sizes where preserving continuous gene-level information is advantageous.

Methodology:

Uses the RNA-Enrich empirical adjustment for average read count per gene and performs logistic regression on continuous gene-level statistics without applying a P-value cutoff.