Abstract
Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample.
Highlights
A cell’s regulome, defined as the activities of all cis-regulatory elements (CREs) in its genome, contains crucial information for understanding how genes’ transcriptional activities are regulated in normal and pathological conditions
SCATE model for a single cell SCATE begins with compiling a list of candidate CREs and grouping co-activated CREs into clusters
Most scATAC-seq data are generated from human and mouse
Summary
A cell’s regulome, defined as the activities of all cis-regulatory elements (CREs) in its genome, contains crucial information for understanding how genes’ transcriptional activities are regulated in normal and pathological conditions. Regulome is measured using bulk technologies such as chromatin immunoprecipitation coupled with sequencing (ChIP-seq [1]), DNase I hypersensitive site sequencing (DNase-seq [2]), and assay for transposase-accessible chromatin followed by sequencing (ATAC-seq [3]). These technologies measure cells’ average behavior in a biological sample consisting of thousands to millions of cells. Single-cell ATAC-seq (scATAC-seq [4, 5]) and
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