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omicverse-single-cell-via-trajectory

Run VIA trajectory inference on single-cell AnnData data.

13|2|Updated Apr 5, 2026
Search Tags:#trajectory#pseudotime#single-cell#anndata#via#rna velocity#lineage analysis
One-click install
npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-via-trajectory
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omicverse-single-cell-via-trajectory
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-via-trajectory
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-via-trajectory

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill removes the guesswork from single-cell trajectory analysis by turning a preprocessed AnnData object into an interpretable VIA lineage map with pseudotime, terminal-state detection, and gene-trend diagnostics.

Core Features & Use Cases

  • Vanilla VIA: Run topology-aware trajectory inference from PCA and embedding coordinates when you only have expression and cluster labels.
  • Velocity-guided VIA: Incorporate RNA velocity, gene expression, and PCA loadings to bias transitions toward more biologically grounded lineage directions.
  • Downstream Interpretation: Inspect cluster-transition graphs, lineage probabilities, stream plots, GAM-fitted trajectories, and per-gene trends to validate the inferred biology.
  • Use Case: A researcher can analyze hematopoietic single-cell data, compare progenitor-to-branch lineage paths, and verify whether marker genes peak in the expected terminal states.

Quick Start

Ask the agent to run VIA on your annotated AnnData object with either vanilla or velocity-guided settings, then return the inferred topology, pseudotime, terminal states, and key gene-trend plots.

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

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

FAQPage Schema
How do I run single-cell trajectory inference on AnnData to get pseudotime and terminal states?▼

Run VIA trajectory inference on your single-cell AnnData object to reconstruct pseudotime, detect terminal states automatically, and visualize gene-trend diagnostics using PCA embeddings and a low-dimensional basis.

Can I use RNA velocity matrices to guide single-cell trajectory analysis with VIA?▼

Yes, RNA velocity-guided VIA incorporates velocity matrices, gene expression, and PCA loadings to bias cluster transitions toward biologically grounded lineage directions, producing more accurate trajectory topology than vanilla VIA.

What inputs do I need to perform pseudotime reconstruction and lineage analysis on single-cell data?▼

You need a preprocessed single-cell AnnData object with PCA embeddings, a low-dimensional basis like UMAP, and cluster labels. Optional RNA velocity matrices and PCA loadings enable velocity-guided VIA workflows.

What downstream visualizations can I generate after running VIA trajectory inference?▼

After running VIA, you can inspect cluster-transition graphs, lineage probabilities, stream plots, GAM-fitted trajectories, and per-gene trends to validate inferred lineage biology and verify marker gene expression in terminal states.

Vanilla VIA vs RNA velocity-guided VIA: which single-cell trajectory method should I use?▼

Use vanilla VIA when you only have expression and cluster labels for topology-aware trajectory inference. Choose velocity-guided VIA when RNA velocity matrices and PCA loadings are available to bias transitions toward biologically grounded lineage directions.

Why does VIA trajectory inference require a low-dimensional basis and PCA embeddings for single-cell data?▼

VIA requires PCA embeddings and a low-dimensional basis because it uses these coordinates to compute topology-aware cluster transitions, reconstruct pseudotime, and project lineage paths accurately across the single-cell manifold.

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