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. 2020 Jun 23;10:10150. doi: 10.1038/s41598-020-66998-4

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The performance of DESeq2, edgeR, and limma on real ATAC-seq data. (A) Principle Component Analysis of mouse liver and kidney ATAC-seq data. (B) Concordance of DARs identified by DESeq2, edgeR, and limma. (C) The effect of sample size on DARs identification by DESeq2, edgeR, and limma. X-axis indicates the sample size, Y-axis is the percentage of sensitivity (Recall rate) and false positive rate (New DARs). (D) Volcano-plot of DARs identified by edgeR. X-axis indicates the ATAC-seq signal difference between liver and kidney. (E) The effect of FDR/adjusted-p-value and signal difference on identification of DARs. X-axis is the absolute signal difference between liver and kidney. (F) Correlation between ATAC-seq signal and RNA-seq signal for DARs overlapping gene promoter. X-axis indicates the log₂ transformed ATAC-seq signal difference between liver and kidney, y-axis indicates log₂-transformed RNA-seq signal difference between liver and kidney.