Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA.
Genome Biol. 2020 Jul 3;21(1):161. doi: 10.1186/s13059-020-02075-3.
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.
单细胞测序分析可及染色质(scATAC-seq)是分析单细胞全基因组调控景观的最新技术。单细胞 ATAC-seq 数据稀疏且嘈杂,分析此类数据具有挑战性。现有的计算方法不能准确重建单个顺式调控元件(CRE)在单个细胞或稀有细胞亚群中的活性。我们提出了一种新的统计框架 SCATE,它自适应地整合来自共激活 CRE、相似细胞和公开可用调控组数据的信息,从而大大提高了估计单个 CRE 活性的准确性。我们证明了 SCATE 可用于更好地重建异质样本的调控景观。