Snakemake workflow: single-cell-counts-preprocessing

A Snakemake workflow for preprocessing of single-cell RNAseq count data following single-cell best practices. It follows the single-cell best practices guide section on Preprocessing and Visualization: https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html It provides quality control (filtering of low quality cells, correction of ambient RNA background, and doublet detection), normalization (shifted logarithm, scran, or Pearson residuals), feature selection and dimensionality reduction (PCA, t-SNE and UMAP).

• Snakemake workflow: single-cell-counts-preprocessing
  • Usage
  • Deployment options
  • Workflow profiles
  • Authors
  • References

Usage

The usage of this workflow is described in the Snakemake Workflow Catalog. This includes a visualization of the workflow diagram and a table with all workflow parameters.

Detailed information about input data and workflow configuration can also be found in the config/README.md.

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository or its DOI, and the References listed below.

Deployment options

To run the workflow from command line, change the working directory.

cd path/to/snakemake-workflow-name

Adjust options in the default config file config/config.yaml. Before running the complete workflow, you can perform a dry run using:

snakemake --dry-run

To run the workflow with test files using conda:

snakemake --cores 2 --sdm conda --directory .test

Workflow profiles

The profiles/ directory can contain any number of workflow-specific profiles that users can choose from. The profiles README.md provides more details.

Authors

• David Lähnemann
  • German Cancer Consortium (DKTK), partner site Essen-Düsseldorf, A partnership between DKFZ and University Hospital Essen
  • https://orcid.org/0000-0002-9138-4112

References

Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-023-00586-w

Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., & Nahnsen, S. Sustainable data analysis with Snakemake. F1000Research, 10:33, 10, 33, 2021. https://doi.org/10.12688/f1000research.29032.2.