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bioRxiv · 10.1101/2023.06.13.544851

Single-cell multiome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

Mitra, S.·Malik, R.·Wong, W.·Rahman, A.·Hartemink, A. J.·Pritykin, Y.·Dey, K. K.·Leslie, C. S.
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Abstract

We present a novel gene-level regulatory model called SCARlink that predicts single-cell gene expression from single-cell chromatin accessibility within and flanking (+/-250kb) the genic loci by training on multiome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on a peak atlas. SCARlink significantly outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across across high-coverage multiome data sets while giving comparable to improved performance on low-coverage data sets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 8x-35x enriched in fine-mapped eQTLs and 22x-35x enriched in fine-mapped GWAS variants across 83 UK Biobank traits. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

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Mitra, S., Malik, R., Wong, W., Rahman, A., Hartemink, A. J., Pritykin, Y., Dey, K. K., Leslie, C. S.. 2023-06-14. Single-cell multiome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis. https://doi.org/10.1101/2023.06.13.544851

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