PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data.
PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data.
复制标题
PACS 允许从 snATAC-seq 数据中全面剖析控制染色质可及性的多个因素。
DOI：
10.1101/2023.07.30.551108
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Kim,Junhyong
中科院分区：
文献类型：
--
作者：
Miao,Zhen;Wang,Jianqiao;Park,Kernyu;Kuang,Da;Kim,Junhyong
单核ATAC-seq（snATAC-seq）实验设计已经变得越来越复杂，具有可能影响染色质可及性的多种因素，包括基因型、细胞类型、来源组织、样品位置、批次等，其复合效果难以通过现有方法测试。此外，目前的snATAC-seq数据由于其稀疏性和个体序列捕获的变化而存在统计困难。为了解决这些问题，我们提出了一个零调整的统计模型，概率模型的单细胞染色质（PACS），可以允许复杂的假设检验的因素，影响可访问性，同时占稀疏和不完整的数据。对于差异可访问性分析，PACS控制了假阳性率，平均比现有工具高出17%至122%。我们证明了PACS的有效性，通过几个分析任务，包括监督细胞类型注释，复合假设检验，批量效应校正，时空建模。我们将PACS应用于来自各种组织的几个数据集，并显示其能够揭示snATAC-seq数据中以前未发现的见解。
Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data.