snakemake-workflows/single-cell-counts-preprocessing

A standardised Snakemake workflow for preprocessing of single-cell RNAseq count data following single-cell best practices.

Overview

Latest release: None, Last update: 2026-09-19

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=snakemake-workflows/single-cell-counts-preprocessing

Quality control: linting: passed formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/snakemake-workflows/single-cell-counts-preprocessing . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow using apptainer/singularity, use

snakemake --cores all --sdm apptainer

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Workflow overview

This workflow is a best-practice workflow for preprocessing counts from single cell RNA sequencing data. The workflow is built using snakemake and follows the Preprocessing and visualization section of the Single Cell Best Practices.

It consists of the following steps:

Convert input data to zarr format.
Filter low-quality barcodes with scanpy.
Correct for ambient RNA contamination with SoupX.
Detect doublets with scDblFinder.
Normalize counts with scanpy.

Running the workflow

To configure the workflow run, go through the provided config/config.yaml entry by entry and adjust them where necessary. After an initial run, we recommend going through the quality control plots mentioned there and double-checking that the provided threshold values for filtering make sense.

Input data

To specify the input, provide a config/sample_sheet.tsv file with the following layout:

sample_id	raw_counts_path	format
cellranger_1	../path/to/raw_feature_bc_matrix/	10x_mtx
kallisto_bustools_1	../path/to/adata.h5ad	h5ad
alevin_fry_1	../path/to/quants_mat.mtx	mtx

Here, the columns are:

sample_id: An arbitrary string identifier of a a sample (or dataset).
raw_counts_path: The path to a file or folder with the raw counts as determined by another tool, for example CellRanger, kallisto bustools or alevin-fry. We really recommend using the raw counts here (and not any pre-filtered counts), as they are instrumental in the correction for ambient RNA contamination. The workflow filters low-quality barcodes itself, and lets you transparently configure and check the filter thresholds (see the comments in config/config.yaml).
format: Format of the input data given in column raw_counts_path. Choose a format that scanpy can read, so any suffix in one of the scanpy.read_ functions. See https://scanpy.scverse.org/en/stable/api/io.html or double-check the code at: https://github.com/scverse/scanpy/blob/a656a33b080a5c1f64b01e841daad76f35f5ec5f/src/scanpy/io/_read.py#L44-L61”

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Description	Required	Default
sample_sheet	string	path to sample sheet, mandatory	yes	config/sample_sheet.tsv
filtering			yes
. counts_mads	integer	Median absolute deviation (MAD) of three counts quality control values, above which a cell is considered an outlier.	yes	5
. mt_percent	integer	Median absolute deviation (MAD) of mitochondrial counts percentage from all counts, above which a cell is considered an outlier.	yes	5