Generate cluster statistics and visualizations for Seurat objects.
npx skills add https://github.com/pwwang/immunopipe --skill seuratclusterstats
Please help me install this Agent Skill. Skill: seuratclusterstats Source: https://github.com/pwwang/immunopipe/tree/main/skills/seuratclusterstats Command: npx skills add https://github.com/pwwang/immunopipe --skill seuratclusterstats
Generates comprehensive cluster statistics and visualizations for Seurat objects to facilitate evaluation of clustering results and interpretation of cluster relationships.
Run SeuratClusterStats on your Seurat object to generate the standard set of cluster statistics and visualizations.
High-intent search queries and answers about installing and using this skill.
To evaluate Seurat cluster quality, generate dimension reduction plots, cluster statistics, and gene expression visualizations directly from your Seurat object to assess clustering results and interpret relationships.
A clustree diagram illustrates cluster relationships and stability across resolutions in scRNA-seq analysis. You need it to visually evaluate how clusters relate and maintain consistency during Seurat clustering workflows.
Yes, you can generate cluster statistics for Seurat subclustering workflows. The process supports both SeuratClustering and SeuratSubClustering objects to evaluate sub-population quality and relationships.
To plot gene expression features across Seurat clusters, apply feature plots and dimension reduction visualizations to your clustered Seurat object. This maps gene expression distributions onto the calculated cluster structures.
Seurat cluster visualization works with metadata by evaluating cluster relationships against variables stored in the Seurat object. This allows you to compare clustering structures directly with sample conditions or QC metrics.
To report scRNA-seq cluster statistics, you need dimension reduction plots, clustree diagrams, gene counts, and feature plots. These visualizations summarize clustering structure and quality for reproducible QC reporting.
ya-wayCluster single-cell RNA-seq data with Seurat or Scanpy workflows.
gexijinAnalyze GEO microarray data with QC, normalization, differential expression, and enrichment.
EpiphyticFilters low-quality cells from AnnData and 10X Genomics single-cell RNA-seq datasets using MAD-based thresholds.
jaechang-hitsAnalyze single-cell RNA-seq data with Scanpy from QC to marker identification.
