cellfate-pseudotime-gene-analysis

To identify genes driving cell fate decisions along pseudotime, apply Adaptive Threshold Regression (ATR) to your single-cell RNA-seq data to discover genes whose expression significantly correlates with the inferred developmental trajectory.

Pseudotime-associated gene discovery uses Adaptive Threshold Regression to find genes whose expression significantly correlates with pseudotime values in single-cell RNA-seq data, pinpointing key regulators of developmental progression.

Yes, you need pre-computed pseudotime values in your annotated AnnData object to perform trajectory inference gene analysis, as the Skill requires these values to map gene expression changes along the developmental trajectory.

Yes, you can find lineage-specific fate genes using density estimation and variability scoring, which identifies genes that specifically drive the progression of particular cell lineages when optional clustering defines the lineages.

The best way to score lineage-specific genes in single-cell trajectory data is using density-based manifold analysis and variability scoring to identify genes specifically driving particular cell lineage progressions from progenitor to terminal states.