geecc

geecc performs gene set enrichment analysis using log-linear models and hypergeometric and chi-squared tests to assess enrichment across two- and three-way contingency tables or cubes for biological categories such as Gene Ontology (GO) terms, sequence length, GC content, chromosomal position, phylostrata, divergence-strata, and differentially expressed genes.

Key Features:

• Log-linear models: Implements log-linear models for analysis of categorical gene set data.
• Statistical tests: Performs hypergeometric and chi-squared tests to evaluate significance of gene set enrichment.
• Multi-categorical analysis: Supports analysis across two or three categorical variables using contingency tables and contingency cubes.
• Supported biological categories: Analyzes Gene Ontology (GO) terms, sequence length, GC content, chromosomal position, phylostrata, divergence-strata, and differentially expressed genes.

Scientific Applications:

• Genomics enrichment analysis: Identifies enriched gene sets to interpret biological processes and pathways.
• Differential expression studies: Assesses enrichment among sets of differentially expressed genes.
• Functional annotation: Links gene sets to functional categories such as GO terms.
• Evolutionary biology: Tests enrichment across phylostrata and divergence-strata to investigate evolutionary patterns.

Methodology:

Applies log-linear models to contingency tables or cubes to perform hypergeometric and chi-squared tests on two- or three-way categorical data.