scNavigator processing pipeline

This repository is a Snakemake pipeline that we use to process public scRNA-seq data in scNavigator. The scope of the main branch is to process the raw data and run basic seurat analysis.

Read the full documentation at https://scn-pipeline.readthedocs.io/.

To install the pipeline, please, clone the main branch and install snakemake

$ git clone https://github.com/ctlab/scn-pipeline.git 
$ cd scn-pipeline
$ conda install -n base -c conda-forge mamba
$ mamba create -c conda-forge -c bioconda -n snakemake snakemake
$ conda activate snakemake

For all the steps in this pipeline we have specified the minimum environment required to run the step, so please consider running this pipeline using:

$ snakemake --use-conda ...

Configuration

You first have to configure the project and provide paths to relevant files and folder. The configuration is storen in configs/config.yaml file and consists of several fields. The only two necessary fields that are required to fill are out_dir and ncbi_dir.

out_dir is a directory that will be used to store results, preliminary results, resources, and logs.

ncbi_dir is a directory that is configured via vdb-config (see README.vdb-config in https://github.com/ncbi/sra-tools) and path to this directory is usually stored in ~/.ncbi/user-settings.mkfg

out_dir: '/path/to/out/dir' # output directory with all the results
ncbi_dir: '/path/to/configured/ncbi/folder' # directory from vdb-config

Running pipeline

Once pipeline is configured, fill the datasets in ./config/dataset.yaml Contents of the file should be just a list of dataset IDs (see example below)

["GSE145241", "GSE116240"]

Once datasets are specified pipeline consists of two main steps:

• Acquiring meta information (we use FFQ + custom scripts to detect single-cell technology and version of chemistry)
• Processing (we use STAR + Seurat for processing and further analysis of the dataset)

To acquire all the meta information run

$ snakemake --use-conda get_all_meta

To process the datasets for which you already have meta information simply run

$ snakemake --use-conda process_all

Results of the pipeline can be found in the out_dir directory that you configured:

• /out_dir/resources - all the resources (whitelists and genome indexes) will be stored here
• /out_dir/logs - all the logs will be stored here
• /out_dir/meta/{dataset} - meta information for the datasets (results of get_all_meta)
• /out_dir/data/samples/{dataset}/{sample} - sample-level analysis and STAR results are stored here
• /out_dir/data/datasets/{dataset}/ - dataset-level integration analysis results are stored here