single-trajectory-analysis

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.
• t_metacells.ipynb: Aggregate metacell trajectories for robustness checks and meta-state differential expression.
• t_cytotrace.ipynb: Integrate CytoTRACE differentiation potential with velocity-informed lineages for maturation scoring.

Required preprocessing

1. Quality control: remove low-quality cells/genes, apply doublet filtering.
2. Normalization & log transformation (sc.pp.normalize_total, sc.pp.log1p).
3. Highly variable gene selection tailored to immune datasets (sc.pp.highly_variable_genes).
4. Batch correction if necessary (e.g., scvi-tools, bbknn).
5. Compute PCA, neighbor graph, and embedding (UMAP/FA) used by all trajectory methods.
6. For velocity: compute moments on the same neighbor graph before running VIA coupling.