Treffer: Best practices for differential accessibility analysis in single-cell epigenomics.

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. Protocol registration: The Stage 1 protocol for this Registered Report was accepted in principle on 25th October 2023. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.24541816.v1. The authors conduct a comprehensive assessment of statistical methods for identifying differentially accessible (DA) regions in scATAC-seq data, using a compendium of scATAC-seq experiments paired with bulk ATAC-seq or scRNA-seq from the same cells to evaluate accuracy, bias, robustness, and scalability. [ABSTRACT FROM AUTHOR]

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