seurat-single-cell-analyzer

name	seurat-single-cell-analyzer
description	Seurat single-cell analysis skill for clustering, annotation, and trajectory analysis
allowed-tools	["Read","Write","Glob","Grep","Edit","WebFetch","WebSearch","Bash"]
metadata	{"version":"1.0","category":"bioinformatics","tags":["transcriptomics","single-cell","clustering","scrnaseq"]}
graph	{"domains":["domain:bioinformatics"],"specializations":["specialization:biomedical-informatics"],"skillAreas":["skill-area:statistical-analysis","skill-area:machine-learning-frameworks","skill-area:data-analysis"],"workflows":["workflow:experiment-design"],"roles":["role:research-scientist","role:biomedical-engineer"]}

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)