omicverse-single-cell-trajectory-inference

Name: omicverse-single-cell-trajectory-inference
Availability: InStock
Author: omicverse

Run OmicVerse single-cell trajectory inference on cluster-ready AnnData objects.

13|2|Updated Apr 5, 2026

Search Tags:#pseudotime #single-cell #omicverse #anndata #palantir #slingshot #trajectory-inference

Ready to run

npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-trajectory-inference-omicverse

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: omicverse-single-cell-trajectory-inference
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-trajectory-inference
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-trajectory-inference-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anndata, numpy, pandas, scanpy, omicverse, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns OmicVerse single-cell trajectory analysis into a reusable workflow for cluster-ready AnnData objects, helping you move from clustered cells to pseudotime, lineage structure, and branch summaries without rebuilding the notebook logic each time.

Core Features & Use Cases

Trajectory branch selection: Choose diffusion_map, slingshot, or palantir based on the biological question and available dependencies.
Lineage and pseudotime outputs: Compute pseudotime, fate probabilities, entropy, branch masks, and PAGA summaries for downstream interpretation.
Follow-up analysis and validation: Support Palantir branch selection, gene trends, and output checks so results can be verified before reuse.
Use case: A researcher with a processed single-cell dataset can run developmental ordering, compare lineage branches, and generate topology-aware plots from the same prepared AnnData object.

Quick Start

Use this skill to analyze your cluster-ready AnnData object with OmicVerse trajectory inference, selecting the appropriate branch and validating the resulting pseudotime and topology outputs.

Frequently Asked Questions about omicverse-single-cell-trajectory-inference

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

FAQPage Schema

How do I run single-cell trajectory inference on an AnnData object?▼

To run single-cell trajectory inference, select the diffusion_map, slingshot, or palantir branch in OmicVerse to calculate pseudotime, fate probabilities, and PAGA topology overlays from your clustered AnnData object.

What is pseudotime estimation and how does it work with single-cell data?▼

Pseudotime estimation orders single cells along continuous developmental trajectories by comparing transcriptomic similarity, using diffusion_map, slingshot, or palantir to infer lineage progression and fate probabilities.

Can I use Palantir and Slingshot for trajectory analysis within the same workflow?▼

Yes, you can select either the Palantir or Slingshot branch within the OmicVerse workflow, choosing based on your biological question and available dependencies to compute lineage-specific pseudotime and topology.

Do I need pre-clustered data and basis coordinates to infer single-cell trajectories?▼

Yes, trajectory inference requires a cluster-ready AnnData object with pre-computed basis coordinates, group labels, and defined origin cells to successfully calculate pseudotime and branch summaries.

What are the limitations of using diffusion maps for single-cell trajectory inference?▼

Diffusion map trajectory inference depends on valid basis coordinates and specific dependencies like pcurvepy2, making it unsuitable if your AnnData object lacks clear origin cells or defined terminal states.