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slingshot-trajectory

Infer differentiation trajectories and pseudotime from Seurat v5 single-cell data using Slingshot.

Updated May 28, 2026
Search Tags:#differentiation#pseudotime#single-cell#seurat#scrna-seq#trajectory inference#slingshot
One-click install
npx skills add https://github.com/cpanse/skills --skill slingshot-trajectory-cpanse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slingshot-trajectory
Source: https://github.com/cpanse/skills/tree/main/single-cell-spatial-general/skills/slingshot-trajectory
Command: npx skills add https://github.com/cpanse/skills --skill slingshot-trajectory-cpanse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Reconstructing cell differentiation paths and ordering cells along developmental timelines from scRNA-seq data requires careful parameter tuning, root cluster selection, and visualization; this Skill provides a complete, FGCZ-standardized Slingshot workflow on Seurat v5 objects. ## Core Features & Use Cases - Trajectory Inference: Run slingshot/slingPseudotime/slingCurves on UMAP, PCA, or WNN embeddings with tunable omega, stretch, and start/end cluster constraints. - CytoTRACE2 Integration: Automatically select the trajectory origin from potency scores or known stem markers. - Rich Visualization: Generate principal curve overlays, pseudotime UMAPs, violin plots by cell type, pseudotime-ordered gene expression heatmaps, and condition comparisons at 300 DPI. - Use Case: Analyze HSPC differentiation by inferring three major lineages (myeloid, erythroid, lymphoid) from a WNN reduction, then produce a pseudotime-ordered marker gene heatmap and per-timepoint pseudotime comparisons. ## Quick Start Ask the agent to run Slingshot trajectory inference on your Seurat object using the WNN UMAP reduction with HSC as the starting cluster and plot the resulting pseudotime.

Frequently Asked Questions about slingshot-trajectory

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

FAQPage Schema
How do I run Slingshot trajectory inference on a Seurat object?▼

Extract embedding coordinates with Embeddings() from a UMAP, PCA, or WNN reduction, then call slingshot() with cluster labels and a start.clus argument. Retrieve results with slingPseudotime() and slingCurves() and add them back to the Seurat metadata.

How do I choose the starting cluster for Slingshot pseudotime?▼

Use the cell type containing the cell with the highest CytoTRACE2 potency score as the origin, or manually specify a known stem or progenitor population such as HSC. The start.clus parameter anchors trajectory direction.

Can Slingshot work with multimodal RNA and ADT data?▼

Yes, Slingshot accepts any dimensional reduction, including weighted nearest neighbor (WNN) UMAP embeddings built from combined RNA and ADT assays in Seurat v5. Pass the WNN reduction coordinates directly to the slingshot() function.

Why does Slingshot detect too many lineages?▼

Excess lineages result from a high omega penalty parameter. Lower omega to 0.3-0.5 to penalize additional lineages, or post-filter by keeping only the top N curves with the most assigned cells.

How do I handle Slingshot memory errors on large datasets?▼

Subsample cells (e.g., 50,000) before running inference, or use PCA coordinates with 20 components instead of UMAP to reduce dimensionality. Pseudotime can then be projected back to the full dataset with nearest-neighbor approaches.

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