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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.
Pubmed ID: 34737275 RIS Download
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Software package for analyzing scATAC-seq datasets.Used to dissects cellular heterogeneity in unbiased manner and map trajectories of cellular states. Can process data from up to million cells. Incorporates existing tools into comprehensive package for analyzing single cell ATAC-seq dataset.
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