Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

scATAC-Seq yields data that is extremely sparse. Here, the authors present a computationally efficient imputation method called scOpen that improves the downstream analyses of scATAC-Seq data and use it to identify transcriptional regulators of kidney fibrosis. A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.

Automated summary

The authors present a computationally efficient imputation method called scOpen to improve downstream analyses of scATAC-Seq data for identifying transcriptional regulators of kidney fibrosis. scOpen is based on regularized non-negative matrix factorization and effectively addresses the sparsity issue in scATAC-Seq data, demonstrating its power in dissecting regulatory changes in kidney fibrosis development.