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omicverse-single-cell-monocle2-trajectory

Fit Monocle2-style trajectories on AnnData to derive pseudotime and branch-dependent gene programs.

13|2|Updated Apr 5, 2026
Search Tags:#trajectory#pseudotime#single-cell#beam#anndata#monocle2#ddrtree
One-click install
npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-monocle2-trajectory
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omicverse-single-cell-monocle2-trajectory
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-monocle2-trajectory
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-monocle2-trajectory

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill turns preprocessed single-cell AnnData into a Monocle2-style trajectory analysis workflow, helping you order cells in pseudotime, detect branches, and identify genes that change along the path.

Core Features & Use Cases

  • Trajectory fitting: Builds a DDRTree-based trajectory with Monocle-style preprocessing, ordering-gene selection, and root-cell assignment.
  • Gene dynamics analysis: Runs differential gene tests along pseudotime and BEAM branch-dependent testing to find meaningful temporal and lineage-specific signals.
  • Visualization: Produces trajectory overlays, branch streamplots, dynamic heatmaps, and per-gene trend plots for publication-ready interpretation.
  • Use case: Analyze a hematopoietic single-cell dataset to compare branch fate decisions, highlight marker shifts over pseudotime, and visualize top branch-specific genes.

Quick Start

Use this Skill to fit a Monocle2 trajectory on your AnnData object, run pseudotime and BEAM analysis, and generate trajectory and gene-trend visualizations.

Frequently Asked Questions about omicverse-single-cell-monocle2-trajectory

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

FAQPage Schema
How do I run pseudotime trajectory analysis on single-cell RNA-seq data using AnnData?▼

You run pseudotime trajectory analysis by fitting a Monocle2-style DDRTree trajectory on an AnnData object to order cells in pseudotime and detect branch points. This requires preprocessed single-cell RNA-seq data containing ordering genes and root-cell assignment metadata.

What is BEAM analysis in single-cell trajectory mapping?▼

BEAM analysis is a branch-dependent testing method that identifies genes changing dynamically along specific lineages in a single-cell trajectory. It detects temporal and lineage-specific signals at branch points to reveal fate decision programs.

Can I visualize branch-specific gene dynamics and pseudotime trends from an AnnData object?▼

Yes, you can visualize branch-specific gene dynamics by generating trajectory overlays, branch streamplots, dynamic heatmaps, and per-gene trend plots. These publication-ready visualizations map gene expression shifts across pseudotime directly from the fitted AnnData trajectory.

Does this Monocle2 trajectory workflow require preprocessed AnnData with specific metadata?▼

Yes, this Monocle2 trajectory workflow requires preprocessed AnnData inputs containing ordering genes and pseudotime metadata. Compatibility with ov.single.Monocle and dynamic plotting helpers ensures proper DDRTree ordering and trajectory fitting.

How do I perform differential gene testing along pseudotime in a single-cell dataset?▼

You perform differential gene testing along pseudotime by applying pseudotime differential tests after fitting a DDRTree trajectory. This identifies genes with significant expression changes over pseudotime and across branch lineages within the single-cell dataset.

What is the best way to identify branch fate decisions in hematopoietic single-cell data?▼

The best way to identify branch fate decisions is to fit a Monocle2-style trajectory, run BEAM branch-dependent testing, and compare lineage-specific gene shifts over pseudotime. This highlights top branch-specific genes and marker changes driving hematopoietic fate decisions.

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