scSTEM

scSTEM clusters genes in pseudotime-ordered single-cell RNA-seq (scRNA-seq) data to identify and assess dynamic gene expression profiles underlying cellular differentiation and development.

Key Features:

• Pseudotime Trajectory Analysis: Analyzes pseudotime-ordered scRNA-seq data to arrange cells along developmental or differentiation trajectories.
• Gene Clustering: Clusters genes based on their expression profiles across inferred pseudotime trajectories to identify groups with similar temporal patterns.
• Statistical Significance Assessment: Summarizes gene expression using defined metrics and assigns p-values to clusters to evaluate statistical significance.
• Comparative Analysis Across Trajectories: Compares gene expression profiles across different pseudotime paths to detect similarities and differences in dynamic processes.

Scientific Applications:

• Biological Process Elucidation: Reveals gene modules and temporal expression patterns that inform mechanisms of cellular differentiation and development.
• Enhanced Downstream Analysis: Refines interpretation of scRNA-seq datasets by providing statistically validated temporal gene expression profiles for downstream functional and regulatory analysis.

Methodology:

Applies clustering algorithms and statistical methods tailored for pseudotime-ordered scRNA-seq data, summarizes gene expression with specific metrics, and calculates p-values for identified clusters.