epigen/scrnaseq_processing_seurat
A Snakemake workflow and MrBiomics module for processing and visualizing (multimodal) sc/snRNA-seq data generated with 10X Genomics Kits or in the MTX matrix file format powered by the R package Seurat.
Overview
Topics: bioinformatics workflow snakemake scrna-seq biomedical-data-science cite-seq sccrispr-seq 10xgenomics snrna-seq single-cell visualization
Latest release: v3.0.0, Last update: 2024-12-18
Linting: linting: failed, Formatting:formatting: failed
Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.
When using Mamba, run
mamba create -c conda-forge -c bioconda --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
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/epigen/scrnaseq_processing_seurat . --tag v3.0.0
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 3: Configure workflow
To configure the workflow, adapt config/config.yml to your needs following the instructions below.
Step 4: Run workflow
The deployment method is controlled using the --software-deployment-method (short --sdm) argument.
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.
You need one configuration file and one annotation file to run the complete workflow. You can use the provided example as starting point. If in doubt read the comments in the config and/or try the default values.
- project configuration (config/config.yaml): different for every project/dataset and configures the analyses to be performed
- sample annotation (sample_annotation): CSV file consisting of three columns
- sample_name: name of the sample (tip: keep it short, but unique)
- path (2 options):
- 10X Genomics output: path to the directory containing the Cell Ranger output folder filtered_feature_bc_matrix/
- MTX files: path to the directory containing the following 3 files:
- matrix.mtx containing the counts
- barcodes.tsv containing the cell barcodes in the first column without header (TSV)
- features.tsv containing the feature/gene names in the first column without header (TSV)
- metadata (optional): path to sample metadata as CSV with the first column being cell barcodes and every other coloumn metadata for the respective barcode/cell
Set workflow-specific resources or command line arguments (CLI) in the workflow profile workflow/profiles/default.config.yaml, which supersedes global Snakemake profiles.
Linting and formatting
Linting results
Using workflow specific profile workflow/profiles/default for setting default command line arguments.
FileNotFoundError in file /tmp/tmpiagy6wvy/epigen-scrnaseq_processing_seurat-78d9af4/workflow/Snakefile, line 24:
[Errno 2] No such file or directory: '/path/to/scrnaseq_processing_annotation.csv'
File "/tmp/tmpiagy6wvy/epigen-scrnaseq_processing_seurat-78d9af4/workflow/Snakefile", line 24, in <module>
File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 620, in _read
File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1880, in _make_engine
File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/common.py", line 873, in get_handle
Formatting results
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