epigen/dea_seurat

A Snakemake workflow and MrBiomics module for performing differential expression analyses (DEA) on (multimodal) sc/snRNA-seq data powered by the R package Seurat.

Overview

Topics: bioinformatics biomedical-data-science differential-expression-analysis scrna-seq single-cell snakemake snrna-seq visualization workflow volcano-plot

Latest release: v2.0.0, Last update: 2024-12-03

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/dea_seurat . --tag v2.0.0

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.

Configuration

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 five columns
    • name: name of the dataset/analysis (tip: keep it short, but descriptive and distinctive).
    • data: path to the input Seurat object as .rds.
    • assay: the Seurat assay to be used (e.g., SCT or RNA).
    • metadata: column name of the metadata that should be used to group cells for comparison (e.g., condition or cell_type).
    • control: name of the class/level that should be used as control in the comparison (e.g., untreated) or "ALL" to compare every class against the rest (e.g., useful to find cluster markers; one vs all)

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/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/Snakefile, line 25:
[Errno 2] No such file or directory: '/path/to/MyData_dea_seurat_annotation.csv'
  File "/tmp/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/Snakefile", line 25, 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

[DEBUG] 
[DEBUG] In file "/tmp/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/rules/visualize.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/rules/envs_export.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/rules/common.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/rules/dea.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmpg5dkdmuv/epigen-dea_seurat-eaf7369/workflow/Snakefile":  Formatted content is different from original
[INFO] 5 file(s) would be changed 😬

snakefmt version: 0.10.2