seurat-single-cell-analyzer

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Seurat single-cell analysis skill for clustering, annotation, and trajectory analysis

General

What this skill does


# Seurat Single-Cell Analyzer Skill

## Purpose
Enable Seurat single-cell analysis for clustering, annotation, and trajectory analysis of scRNA-seq data.

## Capabilities
- Quality filtering and normalization
- Dimensionality reduction (PCA, UMAP)
- Graph-based clustering
- Marker gene identification
- Cell type annotation
- Integration across datasets
- Trajectory inference

## Usage Guidelines
- Apply quality filters appropriate for experiment
- Normalize data before dimensionality reduction
- Select clustering resolution based on biology
- Identify markers for cluster annotation
- Integrate datasets to remove batch effects
- Document analysis parameters

## Dependencies
- Seurat
- Scanpy
- CellRanger

## Process Integration
- Single-Cell RNA-seq Analysis (scrnaseq-analysis)
- Spatial Transcriptomics Analysis (spatial-transcriptomics)

Files: 1

Size: 1.2 KB

Complexity: 8/100

Category: General

Source: https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/bioinformatics/skills/seurat-single-cell-analyzer

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