Preprint available on bioRxiv.
Documentation coming soon!
Single-cell ATAC+RNA linking (SCARlink) uses multiomic single-cell ATAC and RNA to predict gene expression from chromatin accessibility and predict regulatory regions. It uses tile-based chromatin accessibity data from a genomic locus (+/-250kb flanking gene body) and applies regularized Poisson regression to predict gene expression. The learned regression coefficients of the tiles are informative of regulatory regions in a gene-specific manner. The regression model by itself is cell-type-agnostic. Shapley values computed by grouping cells provide insight into the regulatory regions in a cell-type specific manner. Therefore, the trained regression model can be used multiple times to identify important tiles for different groupings without retraining.
To install SCARlink first create a conda environment:
conda create -n scarlink-env python=3.8
conda activate scarlink-envInstall essential R packages
conda install -c conda-forge r-seurat r-devtools r-biocmanager
conda install -c bioconda bioconductor-rhdf5 \
bioconductor-chromvar \
bioconductor-motifmatchr \
bioconductor-oncomix \
bioconductor-complexheatmap
Install ArchR in the conda environment inside R
devtools::install_github("GreenleafLab/ArchR", ref="master", repos = BiocManager::repositories())Download SCARlink from GitHub and install
git clone https://github.com/snehamitra/SCARlink.git
pip install -e SCARlink
SCARlink requires preprocessed scRNA-seq in a Seurat object and scATAC-seq in ArchR object. The cell names in both the objects need to be identical. Please refer to the example notebook for generating Seurat and ArchR objects.
Run scarlink_preprocessing to generate coasssay_matrix.h5 to use as input to SCARlink.
scarlink_processing --scrna scrna_seurat.rds --scatac scatac_archr -o multiome_out
For small data sets with few genes, run SCARlink sequentially on the gene set in the same output directory multiome_out, as before, for all the remaining steps. Note that celltype needs to be present in either scrna_seurat.rds or scatac_archr.
scarlink -o multiome_out -g hg38 -c celltype
2a. Running locally without cell type information during training and computing cell type scores afterwards
SCARlink can also be run without providing cell type information using -c. In that case SCARlink only computes the gene-level regression model and does not estimate cell-type-specific Shapley values.
scarlink -o multiome_out -g hg38
The cell-type-specific Shapley scores can be computed later on by running scarlink with the -c parameter. In that case the previously estimated gene-regression models are used.
scarlink -o multiome_out -g hg38 -c celltype
SCARlink can be run again with a different cell type annotation. For example, more granular annotations. Both celltype and celltype_granular scores would be retained.
scarlink -o multiome_out -g hg38 -c celltype_granular
To speed up computation, SCARlink can be run on a cluster in parallel. By default it parallelizes over 100 cores, -np 100 when -np is not provided.
scarlink -o multiome_out -g hg38 -c celltype -p $LSB_JOBINDEXGet table with tile-level siginificance for each gene and celltype.
scarlink_tiles -o multiome_out -c celltype Chromatin potential analysis can be performed after running steps 1 and 2. Please refer to example notebook.
SCARlink can create visualizations for the chromatin accessibility around a gene (250kb upstream and downstream of the gene body) and the gene expression for a given gene, grouped by user-provided celltype. The visualizations can help understand the connection between the accessibility at tile-level to gene expression. Note that SCARlink can generate visualizations for different cell type groupings on the fly. Example visulizations are provided in a notebook. The visualizations can also be generated at the command line.
scarlink_tiles -o multiome_out -c celltype --genes GENE1,GENE2