Fates

Fates infers dynamic cellular trajectories and bifurcation events from single-cell RNA and ATAC sequencing data to derive pseudotime and identify lineage-specific gene expression changes.

Key Features:

• Tree learning: Employs algorithms to construct trajectory trees representing cellular developmental processes and bifurcations.
• Pseudotime and bifurcation analysis: Computes pseudotime ordering and detects bifurcation points along trajectories.
• Feature association testing: Tests associations between features such as genes or chromatin accessibility peaks and trajectory positions or branches.
• Branch differential expression analysis: Performs differential expression analysis between trajectory branches to identify branch-specific genes.
• Cell biasing and fate splits: Analyzes cell biasing at bifurcations to characterize fate decisions and lineage bias.
• Integration with scanpy: Integrates trajectory analysis within the scanpy ecosystem for compatibility with AnnData-based workflows.

Scientific Applications:

• Developmental biology: Dissects dynamic gene expression and lineage bifurcations during organismal or tissue development using single-cell RNA and ATAC data.
• Stem cell research: Identifies fate decisions and regulatory genes underlying stem cell differentiation trajectories.
• Disease progression modeling: Maps cellular state transitions and branch-specific molecular changes associated with disease progression.
• Regulatory mechanism discovery: Reveals candidate regulatory genes and chromatin accessibility changes underlying state transitions and fate decisions.

Methodology:

Implements tree learning and pseudotime and bifurcation analyses within the scanpy framework, performs feature association testing and branch differential expression analysis, and analyzes cell biasing and fate splits on single-cell RNA and ATAC sequencing data.