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cytotrace2-analysis

Predict cellular potency and differentiation states from scRNA-seq Seurat objects using CytoTRACE2.

Updated May 28, 2026
Search Tags:#bioinformatics#r#differentiation#seurat#scrna-seq#cytotrace2#cell potency
One-click install
npx skills add https://github.com/cpanse/skills --skill cytotrace2-analysis-cpanse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cytotrace2-analysis
Source: https://github.com/cpanse/skills/tree/main/single-cell-spatial-general/skills/cytotrace2-analysis
Command: npx skills add https://github.com/cpanse/skills --skill cytotrace2-analysis-cpanse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Inferring cellular differentiation potential and stemness from single-cell RNA-seq data requires specialized tooling, careful input handling, and significant compute resources for large datasets, which often leads to cryptic failures and inconsistent reporting. ## Core Features & Use Cases - Potency Scoring: Run CytoTRACE2 on Seurat objects to produce continuous scores, categorical potency labels (Differentiated through Totipotent), and dataset-relative rankings. - Large-Dataset Workflows: Split-by-sample processing, SBATCH cluster job templates, and resource guidelines for datasets exceeding 100K cells. - Reporting & Visualization: FGCZ-standard R Markdown report template with UMAP feature plots, violin plots with potency axes, compositional bar plots, and gene-correlation analysis. - Use Case: Given a 298K-cell tumor atlas Seurat object, split it by patient sample, run CytoTRACE2 on a cluster via SBATCH, and generate a report ranking malignant cell stemness with correlated gene signatures. ## Quick Start Ask the agent to run CytoTRACE2 potency analysis on your Seurat object with raw counts, splitting by sample if the dataset exceeds 100K cells.

Frequently Asked Questions about cytotrace2-analysis

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

FAQPage Schema
How do I run CytoTRACE2 on a Seurat object?▼

Call cytotrace2() with is_seurat = TRUE, slot_type = "counts", and your species ("human" or "mouse"). The input must contain raw counts, not log-transformed data, with no duplicate gene names or cell IDs.

How to run CytoTRACE2 on large scRNA-seq datasets over 100K cells?▼

Split the Seurat object by sample with SplitObject, run cytotrace2 on each subset, then merge and transfer the score metadata back. For very large datasets, submit an SBATCH cluster job with 32 cores and 12G memory per CPU.

Why does CytoTRACE2 fail with "rowMeans(): 'x' must be numeric"?▼

This error on datasets over 100K cells is a resource limitation, not a data quality problem. Validate with a 1000-cell subset first, then run the full dataset as an SBATCH cluster job with adequate memory.

What is the difference between CytoTRACE2 relative and absolute scores?▼

The relative score is normalized within the dataset for ranking cells against each other, while the absolute score (0 to 1) is comparable across datasets. Absolute scores map to potency categories from Differentiated to Totipotent.

Can CytoTRACE2 use log-transformed expression data?▼

No, CytoTRACE2 requires raw count data via slot_type = "counts". For Seurat v5 objects, use JoinLayers() first and access counts with layer = "counts" rather than the older slot syntax.

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