scFates

scFates performs pseudotime and bifurcation analysis of single-cell RNA-seq and ATAC-seq data to infer cellular trajectories and identify branch-specific gene expression changes.

Key Features:

• Tree Learning: Learns tree structures from single-cell datasets to infer developmental pathways and lineage relationships.
• Feature Association Testing: Tests associations between features (e.g., gene expression levels) and pseudotime or bifurcation points to identify regulators of cell fate decisions.
• Branch Differential Expression Analysis: Performs differential expression analysis across trajectory branches to detect branch-specific gene expression changes.
• Cell Biasing and Fate Splits: Analyzes cell biasing and fate splits at bifurcation points to characterize decision-making processes during differentiation.

Scientific Applications:

• Single-cell RNA-seq analysis: Infers pseudotemporal orderings and branch-specific transcriptional programs from scRNA-seq data.
• Single-cell ATAC-seq analysis: Applies trajectory and bifurcation analysis to chromatin accessibility data to study regulatory changes along lineages.
• Developmental biology: Resolves developmental trajectories and lineage branching to study differentiation processes.
• Cancer progression studies: Identifies divergent cellular states and branch-specific expression changes relevant to tumor evolution.

Methodology:

Integration with scanpy and support for multiple data modalities, implementing pseudotime and bifurcation analysis, tree learning, feature association testing, branch differential expression, and analysis of cell biasing at bifurcations.