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seurat-single-cell-analyzer

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

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Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/science/bioinformatics/skills/seurat-single-cell-analyzer/SKILL.md

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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)