Single-cell clustering and batch correction with omicverse
CommunityCluster & correct single-cell batches.
Education & Research#quality control#bioinformatics#single-cell clustering#batch correction#dimensionality reduction#omicverse#data integration
AuthorStarlitnightly
Version1.0.0
Installs0
System Documentation
What problem does it solves? Analyzing single-cell data often involves complex preprocessing, robust clustering, and correcting for technical batch effects. This Skill provides a comprehensive workflow to handle these challenges efficiently, from initial quality control to advanced integration and clustering.
Core Features & Use Cases
- Quality Control & Preprocessing: Filter low-quality cells, normalize, scale, and reduce dimensionality.
- Multi-Method Clustering: Apply and evaluate various clustering algorithms (Leiden, scICE, GMM, cNMF, LDA).
- Batch Correction & Integration: Harmonize datasets using methods like Harmony, ComBat, Scanorama, scVI, and CellANOVA.
- Use Case: Process a multi-batch single-cell RNA-seq experiment, apply Harmony for batch correction, then cluster the integrated data using scICE, and benchmark the integration performance to ensure robust biological insights.
Quick Start
Preprocess my single-cell data, apply Harmony for batch correction, then cluster using Leiden, and visualize the UMAP embedding.
Dependency Matrix
Required Modules
omicversescanpyscvelonumpypandasmatplotlibstatsmodelsscib-metricsscvi-toolsrpy2
Components
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Single-cell clustering and batch correction with omicverse Download link: https://github.com/Starlitnightly/omicverse/archive/main.zip#single-cell-clustering-and-batch-correction-with-omicverse Please download this .zip file, extract it, and install it in the .claude/skills/ directory.