flexgsea

flexgsea performs gene set enrichment analysis to identify coordinated changes in predefined gene sets from genome-wide RNA expression data for interpretation of biological pathways and functions.

Key Features:

• Implementation: Provided as an R package for conducting gene set enrichment analyses.
• User-defined differential expression: Allows users to incorporate preferred packages or functions for differential expression analysis.
• Significance via sample permutation: Estimates statistical significance by permuting samples.
• Parallelization with 'foreach': Supports parallel computation using the foreach package.
• Weighted KS-like statistic: Implements a weighted Kolmogorov–Smirnov-like statistic to evaluate gene set enrichment.
• Normalized Enrichment Scores (NES): Computes NES and assesses their significance across samples or conditions.

Scientific Applications:

• Cancer research: Detects pathway-level similarities across independent studies, including analyses of patient survival in lung cancer.
• Biomedical transcriptomics: Interprets genome-wide RNA expression data by focusing on predefined gene sets representing biological functions, chromosomal locations, or regulatory mechanisms.

Methodology:

Implements the Gene Set Enrichment Analysis framework using predefined gene sets, a weighted KS-like statistic, sample permutation to assess significance, computation of normalized enrichment scores (NES), support for user-supplied differential expression functions, and parallelization via the foreach package.