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