Journal Article

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

Kramann, Rafael, Zenke, Martin, Costa, Ivan G., Kuppe, Christoph, Li, Zhijian, Cheng, Mingbo, Menzel, Sylvia, Ziegler, Susanne, Kabgani, Nazanin

2021

Overview

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. 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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/1305 / 631/114/2114 / 692/4022/1585/3182 / Binding sites / Biomedical engineering / Chromatin / Chromatin - metabolism / Clustering / Computer applications / Datasets / Deoxyribonucleic acid / DNA / DNA - metabolism / Fibrosis / Genomics / Humanities and Social Sciences / Kidneys / Methods / multidisciplinary / Nephrology / Regulatory sequences / Runx1 protein / Science / Science (multidisciplinary) / Sequence Analysis, DNA - methods / Sequence Analysis, DNA - standards / Single-Cell Analysis - methods / Sparsity / Transcription / Visualization