Single-Cell RNA-seq Analysis Workflow for 10x GENOMICS data

Author

Thomas Vannier (@metavannier), https://centuri-livingsystems.org/t-vannier/ Léa Chabot (@LeaChabot), https://centuri-livingsystems.org/l-chabot/

About

This workflow performs a Snakemake pipeline to process 10x single-cell RNAseq data from fastq files to the analysis of the differential expression of marker-genes. Correction for technical differences between datasets can be included (i.e. batch effect correction) with the integration method during the sctransform process in Seurat to perform comparative scRNA-seq analysis across experimental conditions.

Steps for the analysis:

• cellranger.smk: Build the reference, count and aggregate with cellranger v6.0.0.
• seurat.smk: Run seurat v4.0.3 for the clustering.
• differential_exp.smk: Differential expression based on the non-parametric Wilcoxon rank sum test. DE testing is performed on measured data.
• diffexp_subset.smk: Differential expression analyses inside cluster after a subset on the marker gene expression.

Usage

You need to install Singularity on your computer. This workflow also work in a slurm environment.

Each snakemake rules call a specific conda environment. In this way you can easily change/add tools for each step if necessary.

Step 1: Install workflow

You can use this workflow by downloading and extracting the latest release. If you intend to modify and further extend this workflow or want to work under version control, you can fork this repository.

We would be pleased if you use this workflow and participate in its improvement. If you use it in a paper, don't forget to give credits to the author by citing the URL of this repository and, if available, its DOI.

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files and repositories:

• 00_RawData need the fastq file of each run to analyse.
• 01_Reference need the reference genome in fasta and the corresponding gff for the cellranger mapping step.
• config.yaml indicating the parameters to use.
• Comment the Snakefile on the input line not expected for the pipeline.
• Build the singularity image of mambaforge:23.1.0-1 and cellranger v6.0.0 (file to large for a github repository):

singularity build mambaforge:23.1.0-1.sif docker://condaforge/mambaforge:23.1.0-1 mv mambaforge:23.1.0-1.sif 02_Container/

singularity build cellranger.sif docker://litd/docker-cellranger:v7.1.0 mv cellranger.sif 02_Container/

Step 3: Execute workflow

On your cumputer

• You need Singularity v3.5.3 installed on your computer or cluster.

• Load snakemake from a docker container and run the workflow from the working directory by using these commands:

singularity run --bind ${PWD}:${PWD} docker://snakemake/snakemake:v6.3.0

• Then execute the workflow locally via

snakemake --use-conda --use-singularity --cores 12

On a cluster

• Adapt the batch scripts run_slurm.sh to run your snakemake from the working directory

It will install snakemake with pip and run the workflow in the HPC:

sbatch run_slurm.sh

Step 4: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via:

snakemake --report global_report.html