SEPA

SEPA classifies genes by temporal expression patterns in single-cell RNA sequencing (RNA-seq) data to identify dynamic expression categories for downstream Gene Ontology (GO) enrichment and biological interpretation.

Key Features:

• Gene Expression Pattern Assignment: Classifies genes into temporal expression categories such as constant, monotone increasing, and increasing then decreasing.
• Timepoint and Pseudo-time Support: Accepts true experimental time points or pseudo-time cell ordering as input for temporal pattern analysis.
• GO Enrichment Analysis: Performs Gene Ontology (GO) enrichment analysis on gene sets assigned to specific temporal expression categories.
• R/Bioconductor Implementation: Implemented in R and leverages Bioconductor packages for the computational analyses.

Scientific Applications:

• Single-Cell RNA-seq Data Analysis: Enables examination of gene expression dynamics at single-cell resolution in RNA-seq datasets.
• Temporal Gene Expression Studies: Supports studies using true time points or pseudo-time ordering to investigate temporal changes in gene expression, cellular heterogeneity, and lineage specification.

Methodology:

Categorizes genes by temporal expression patterns (constant, monotone increasing, increasing then decreasing) using either true time points or pseudo-time ordering, then performs Gene Ontology (GO) enrichment analysis; implemented in R within the Bioconductor framework leveraging Bioconductor packages.