Single-trajectory analysis skill

Overview

This skill describes how to reproduce and extend the single-trajectory analysis workflow in omicverse, combining graph-based trajectory inference, RNA velocity coupling, and downstream fate scoring notebooks.

Trajectory setup

• PAGA (Partition-based graph abstraction)
  • Build a neighborhood graph (pp.neighbors) on the preprocessed AnnData object.
  • Use tl.paga to compute cluster connectivity and tl.draw_graph or tl.umap with init_pos='paga' for embedding.
  • Interpret edge weights to prioritize branch resolution and seed paths.
• Palantir
  • Run Palantir on diffusion components, seeding with manually selected start cells (e.g., naïve T cells).
  • Extract pseudotime, branch probabilities, and differentiation potential for subsequent overlays.
• VIA
  • Execute via.VIA on the kNN graph to identify lineage progression with automatic root selection or user-defined roots.
  • Export terminal states and pseudotime for cross-validation against PAGA and Palantir results.

Velocity coupling (VIA + scVelo)

• Use scv.pp.filter_and_normalize, scv.pp.moments, and scv.tl.velocity to generate velocity layers.
• Provide VIA with adata.layers['velocity'] to refine lineage directionality (via.VIA(..., velocity_weight=...)).
• Compare VIA pseudotime with scVelo latent time (scv.tl.latent_time) to validate directionality and root selection.

Downstream fate scoring notebooks

• t_cellfate*.ipynb: Map lineage probabilities onto T-cell subsets, quantify fate bias, and visualize heatmaps.