A comprehensive comparison of differential accessibility analysis methods for ATAC-seq data

Conclusions: It is important to use PCA to check the samples distribution, and the Remove Unwanted Variation strategy can be used to correct the data to improve the sensitivity when strong batch effects are found in the data. Finally, BeCorrect can be used to correct the batch-effect of ATAC-seq data signal based on DARs analysis, and generate a proper visualization on a genome browser.结论：采用主成分分析（Principal Component Analysis, PCA）核查样本分布具有重要意义；当数据中存在显著批次效应时，可通过去除非必要变异（Remove Unwanted Variation）策略对数据进行校正，以提升检测灵敏度。最后，可基于差异可及区域（Differential Accessible Regions, DARs）分析，使用BeCorrect工具校正ATAC-seq（Assay for Transposase-Accessible Chromatin using sequencing）数据信号的批次效应，并在基因组浏览器上生成规范的可视化结果。