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seuratclusteringofallcells

Cluster all cells at coarse resolution using Seurat FindClusters.

22|4|Updated May 18, 2021
Search Tags:#clustering#pca#umap#seurat#scrna-seq#cell-type-identification#torbcellselection
One-click install
npx skills add https://github.com/pwwang/immunopipe --skill seuratclusteringofallcells
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seuratclusteringofallcells
Source: https://github.com/pwwang/immunopipe/tree/main/skills/seuratclusteringofallcells
Command: npx skills add https://github.com/pwwang/immunopipe --skill seuratclusteringofallcells

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Performs coarse clustering on ALL cells (including T cells, B cells, and non-T/B cells) before cell type selection. This process identifies broad cell populations to enable downstream T/B cell selection.

Core Features & Use Cases

  • Coarse clustering for mixed populations: Identify major lineages before finer analysis.
  • Upstream to TOrBCellSelection: Prepares data for downstream T/B cell identification.
  • Config-driven Seurat workflow: Uses FindNeighbors, RunUMAP, FindClusters with configurable parameters.

Quick Start

Provide your SeuratPreparing object and run the SeuratClusteringOfAllCells process to generate coarse clusters before T/B cell selection.

Frequently Asked Questions about seuratclusteringofallcells

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

FAQPage Schema
How do I perform coarse clustering on mixed scRNA-seq cell populations before cell type identification?▼

Coarse clustering on mixed scRNA-seq populations is performed using a Seurat workflow with FindNeighbors, RunUMAP, and FindClusters to reveal broad cell lineages before downstream cell type selection. This identifies major populations in your dataset.

What is the purpose of clustering all cells at a coarse resolution in scRNA-seq analysis?▼

Clustering all cells at a coarse resolution in scRNA-seq analysis identifies broad cell populations, such as T, B, and non-T/B cells. This serves as an upstream step to prepare data for downstream finer cell type identification.

Do I need to prepare my Seurat object before running coarse clustering on all cells?▼

Yes, you need a prepared Seurat object from an upstream SeuratPreparing step before running coarse clustering. The clustering workflow requires this prepared data to properly execute dimensional reduction and neighbor finding.

Can I configure the UMAP and clustering parameters when identifying broad cell populations?▼

Yes, you can configure the parameters for FindNeighbors, RunUMAP, and FindClusters through config sections. This allows you to adjust the coarse clustering resolution to suit your specific mixed T, B, and non-T/B cell dataset.

What should I do after coarse clustering reveals broad T, B, and non-T/B cell populations?▼

After coarse clustering reveals broad T, B, and non-T/B cell populations, you should proceed to downstream TOrBcellSelection. This downstream step isolates and identifies specific T or B cells from the major lineages.

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