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A Snakemake workflow for analysing single cell data obtained from Cell ranger platform using Seurat package

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manuelphilip/Single-cell-analysis-Snakemake-workflow

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Snakemake workflow: SingleCell-10X-Genomics-RNA-seq

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A Snakemake workflow for single-cell data analysis of 10X genomics data including cell type annotation, differential expression (marker gene identification), scRNA-seq integration

Usage

The usage of this workflow is described in the Snakemake Workflow Catalog.

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) sitory and its DOI (see above).

Description

A Snakemake workflow designed to analyse single-cell data from cellranger count output. The workflow expects the cellranger count output which contains per sample bc_matrix (raw and filtered) under the ~/sample_name/outs folder. For more information please refer cellranger ouput

The general steps are as follows: All the steps are carried out using Seurat

  • Preprocessing
  • Clustering and dimensional reduction
  • Marker identification
  • Assigning cell types to clusters (Automate cell type assignment using marker genes)
  • Integrative analysis
  • Default differential expression tests across models
  • Differential expression across samples within the same cell types
  • Gene Ontology analysis using clusterProfiler clusterProfiler
  • Pathway enrichment analysis using clusterProfiler

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A Snakemake workflow for analysing single cell data obtained from Cell ranger platform using Seurat package

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