Defitech Center for Interventional Neurotherapies (.NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland.
NeuroX Institute and Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Nat Commun. 2024 Oct 11;15(1):8805. doi: 10.1038/s41467-024-53089-5.
Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices.
单细胞表观基因组学数据的差异可及性 (DA) 分析可发现调控程序，从而确定细胞类型的身份，并指导对生理和病理生理扰动的反应。虽然已经开发了许多用于识别 DA 区域的统计方法，但决定这些方法性能的原则尚不清楚。因此，对于单细胞表观基因组学数据的 DA 分析，哪种统计方法最合适尚无共识。在这里，我们对已应用于识别单细胞 ATAC-seq (scATAC-seq) 数据中 DA 区域的统计方法进行了系统评估。我们利用大量具有匹配批量 ATAC-seq 或 scRNA-seq 的 scATAC-seq 实验，以评估每种统计方法的准确性、偏差、稳健性和可扩展性。我们实验的结构还为超越 DA 本身的 scATAC-seq 数据分析提供了定义最佳实践的机会。我们利用这一理解开发了一个实现这些最佳实践的 R 包。
