trajectory-lineage

Infer pseudotime and lineage branches from single-cell data by running a Scanpy-based workflow on a processed object with embeddings and annotations. The analysis outputs pseudotime assignments, lineage states, and dynamic gene programs for downstream interpretation of developmental progression.

Trajectory analysis maps cellular trajectories from pseudotime to reveal lineage relationships and dynamic gene modules along developmental progressions. You need it when a processed scRNA-seq dataset requires identifying state transitions, lineage branches, and dynamic gene programs to understand cellular development.

Yes, trajectory analysis supports topology validation, root and branch specification, trajectory inference, and dynamic feature discovery using Python with Scanpy and optional scvelo. It identifies cell state transitions and outputs dynamic gene programs suitable for downstream interpretation.

Yes, trajectory inference requires a processed single-cell object with existing embeddings and annotations. The workflow applies topology validation, root and branch specification, and trajectory inference to this pre-processed input to output pseudotime assignments and lineage states.

Contextual visualization of trajectory lineage is achieved using Python with Scanpy and optional scvelo. The workflow supports topology validation, trajectory inference, and contextual visualization to map cellular trajectories from pseudotime assignments and lineage states.