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cellfate-pseudotime-gene-analysis

Identify pseudotime-associated and lineage-specific fate-driving genes from single-cell expression data.

32|5|Updated Mar 12, 2026
Search Tags:#pseudotime#single-cell#mellon#ridge-regression#fate-driving-genes#adaptive-threshold-regression#lineage-scoring
One-click install
npx skills add https://github.com/omicverse/omicclaw --skill cellfate-pseudotime-gene-analysis-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cellfate-pseudotime-gene-analysis
Source: https://github.com/omicverse/omicclaw/tree/main/src/omicverse_skills/skills/single-cellfate-analysis
Command: npx skills add https://github.com/omicverse/omicclaw --skill cellfate-pseudotime-gene-analysis-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill identifies genes whose expression changes along pseudotime and pinpoints lineage-specific fate-driving genes, removing manual trial-and-error and ad hoc filtering in trajectory analyses.

Core Features & Use Cases

  • Adaptive Threshold Regression (ATR): iteratively removes low-impact genes while preserving R² to select a minimal explanatory gene set.
  • Lineage scoring with Mellon density: detects low-density transition regions on the manifold and scores lineage-specific variability to find fate drivers.
  • Integration and modes: operates on AnnData (.X) with pseudotime in adata.obs, supports ATAC peak mode, optional data augmentation for noisy pseudotime, and GPU/CPU fallback for ridge fitting.
  • Use Case: identify top fate-driving genes along a Palantir or diffusion pseudotime, visualize ATR filtering curves, and produce lineage-specific gene rankings for downstream validation.

Quick Start

Run CellFateGenie on an AnnData object with pseudotime in adata.obs to select pseudotime-associated genes and score lineage-specific fate drivers.

Frequently Asked Questions about cellfate-pseudotime-gene-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify fate-driving genes along a pseudotime trajectory in single-cell data?▼

You identify fate-driving genes by applying adaptive threshold regression and lineage scoring to single-cell expression data with precomputed pseudotime. This iteratively filters low-impact genes while preserving R² to pinpoint lineage-specific fate drivers.

What is adaptive threshold regression for pseudotime-associated gene selection?▼

Adaptive threshold regression (ATR) selects pseudotime-associated genes by iteratively removing low-impact genes while preserving R². This yields a minimal explanatory gene set for single-cell trajectory analysis without ad hoc filtering.

How do I detect low-density transition regions to find fate-driving genes?▼

You detect low-density transition regions by integrating Mellon density estimation with lineage scoring. This scores lineage-specific variability across the manifold to pinpoint genes driving cell fate decisions.

Can I use pseudotime gene analysis with single-cell ATAC peak data?▼

Yes, pseudotime gene analysis supports single-cell ATAC peak mode. The method processes the AnnData expression matrix in .X with precomputed pseudotime in adata.obs to perform feature selection and transition detection.

Do I need precomputed pseudotime and clustering to run lineage scoring?▼

Yes, lineage scoring requires precomputed pseudotime in adata.obs and clustering results. The expression matrix must reside in .X, while Mellon and ridge regression utilities are optional dependencies for low-density estimation and feature selection.

What is the best way to handle noisy pseudotime when selecting fate-driving genes?▼

To handle noisy pseudotime, enable optional data augmentation before running adaptive threshold regression. This stabilizes the selection of fate-driving genes and lineage scoring despite noise in the single-cell trajectory.

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