SeqGSEA

SeqGSEA performs integrated analysis of differential expression and alternative splicing in RNA-Seq datasets to produce composite per-gene scores for cutoff-free gene set enrichment analysis that links transcriptional and post-transcriptional variation to phenotypes.

Key Features:

• Integrated Analysis Pipeline: Calculates differential splicing and differential expression scores per gene and integrates them into a composite per-gene score without applying arbitrary cutoffs.
• Gene Set Enrichment Analysis (GSEA): Performs cutoff-free GSEA using the composite per-gene scores to identify biologically meaningful pathways and networks while mitigating bias toward longer or more highly expressed genes.
• Statistical Rigor: Determines statistical significance via subject permutation tests and requires a minimum of five samples per group for analysis.
• Parallel Processing Capability: Supports parallel execution to reduce computation time for comprehensive analyses.
• Biological Insight from Dual Signals: Integrates differential expression and splicing information to reveal mechanisms involving both transcriptional and post-transcriptional regulation.

Scientific Applications:

• Cancer genomics: Identifies pathways and networks differentially regulated at both expression and splicing levels to inform disease mechanism studies and potential therapeutic targets.
• Developmental biology: Reveals coordinated changes in gene expression and alternative splicing associated with developmental processes.
• Neurobiology: Detects expression- and splicing-level pathway alterations relevant to neural development and neurological disorders.

Methodology:

Models RNA-Seq read count data with a negative binomial distribution; computes differential expression and differential splicing scores per gene; integrates these scores into a composite per-gene score for cutoff-free GSEA; assesses significance using subject permutation tests with a minimum of five samples per group and supports parallel execution.