Sub-GSE

Sub-GSE identifies subsets within predefined gene sets or pathways that are significantly associated with specific phenotypes or cell states using gene expression and phenotypic data for refined gene set enrichment analysis.

Key Features:

• Subset-level enrichment testing: Tests the significance of smaller subsets within predefined gene sets or pathways rather than evaluating entire gene sets.
• Data integration: Leverages gene expression data alongside phenotypic information to assess associations.
• Database support: Integrates functionally related gene sets from Gene Ontology (GO), KEGG, and BioCarta.
• Enhanced sensitivity: Demonstrates superior sensitivity for detecting phenotype-associated gene sets when only a fraction of genes in a set are relevant.
• Biological interpretability: Pinpoints biologically meaningful gene subsets within pathways to improve interpretation compared with individual-gene analyses.
• Empirical evaluation: Validated on both simulated and real datasets.

Scientific Applications:

• Phenotype and cell-state association discovery: Identify gene subsets within pathways that are associated with specific phenotypes or cell states.
• Pathway-focused interpretation: Refine pathway-level conclusions by detecting the subset of genes driving enrichment signals.
• Comparative analysis: Provide a more robust alternative to single-gene analyses for uncovering biologically relevant signals.
• Method benchmarking: Assess enrichment detection sensitivity using simulated and real datasets.

Methodology:

Performs subset-level significance testing of predefined gene sets using gene expression data and phenotypic information and integrates gene sets from Gene Ontology (GO), KEGG, and BioCarta; evaluated on simulated and real datasets.