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.

中文翻译：

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使用 SCATE 提取和增强单细胞 ATAC-seq 信号

转座酶可及染色质的单细胞测序测定 (scATAC-seq) 是分析单细胞全基因组调控景观的最先进技术。单细胞 ATAC-seq 数据稀疏且嘈杂，分析此类数据具有挑战性。现有的计算方法无法准确重建单个细胞或稀有细胞亚群中单个顺式调控元件（CRE）的活性。我们提出了一个新的统计框架 SCATE，它自适应地整合来自共同激活的 CRE、类似细胞和公开可用的调节组数据的信息，以大幅提高估计单个 CRE 活动的准确性。我们证明 SCATE 可用于更好地重建异质样本的监管环境。