Pipeline status

multiSAP is a Snakemake pipeline that performs a comprehensive single-cell Multiome data analysis, covering from the basic steps (QC, alignment, quantification) to more advanced downstream analyses.

multiSAP makes extensive use of Snakemake's integration with the conda package manager, to automatically take care of software requirements and dependencies. Furthermore, it executes cellranger-arc count on FASTQ files produced using 10X Genomics kits. The pipeline's flexibility allows users to adjust it to the distinct experimental characteristics.

• Coral Fustero-Torre
• Yu-Hsin Josch Hsieh

For setting up the pipeline, three configuration files need to be modified. A general description of these files follows. See the Usage section for more details.

• config.yaml contains all pipeline parameters.
• units.tsv contains information on the samples to be analysed and their paths.
• samples.tsv: contains information about the experimental conditions.

• ATAC and GEX raw data in gzip compressed FASTQ files

multiSAP requires the conda package manager in order to work. Please install conda by following the bioconda installation instructions. In addition, of course, it is essential to install Snakemake; following the steps in the Snakemake documentation.

To run the pipeline, the user needs to create the conda environments first, which will take some minutes. This step is done automatically using this command:

snakemake --use-conda --conda-create-envs-only --conda-frontend mamba

Use git clone command to create a local copy.

git clone https://github.com/cfusterot/multiSAP.git

Before executing the pipeline, the users must configure it according to their samples. To do this, they must fill these files:

TIP: different analysis can be run using just one cloned repository. This is achieved by changing the outdir and logdir in the configuration file. Also different parameters values can be used in the different analysis.

This is the pipeline configuration file, where you can tune all the available parameters to customise your scATAC-seq analysis.

This file is used to configure the raw input files.

An example file (template_units.tsv is included in the repository.

Rename it to units.tsv and edit its contents according to the following table:

Note: each sample path should point at a folder containing the GEX and ATAC data separately

├── sample1
│   ├── GEX
│   ├── ATAC
├── sample2
│   ├── GEX
│   ├── ATAC

This table contains the name of each sample and the experimental condition it belongs to.

An example file (template_samples.tsv) is included in the repository. Rename it to samples.tsv and edit its contents.

If you are interested in running the cellbender module via the gpu computation node, a few modifications should be done to the snakemake cluster profiles. The updated configuration steps can be found here: (hpc-config)

Add this folder to the ~/.config/snakemake path in order to make it effective.

Once the pipeline is configured and conda environments are created, the user just needs to run scASAP.

snakemake --use-conda --use-envmodules --jobs 10 --profile hpc-cluster --cache --rerun-incomplete 

The mandatory arguments are:

• --use-conda: to install and use the conda environemnts.
• --use-envmodules: to install and use the module environments.
• -j: number of threads/jobs provided to snakemake.

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

snakemake --report report.html

This report can, e.g., be forwarded to your collaborators. An example (using some trivial test data) can be seen here.