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. 2019 Jan 11;20:35. doi: 10.1186/s12864-018-5390-6

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© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 8 — Sensitivity, precision and AUCOR in the 3-versus-3 split of Bottomly dataset. We split Bottomly dataset into a 3-versus-3 evaluation dataset and 7-versus-8 verification dataset. We take the sets of DE genes for the verification dataset by all considered DE methods as truth (we exclude SAMseq, since it could hardly produce adjusted p-values less than 0.05). Finally, we have 6 true sets from different DE methods. We then calculate sensitivity and precision values for the results of the evaluation dataset using these 6 true sets in turn. DE method for the verification dataset is labeled on the top of each plot. a True set of DE genes from edgeR. b True set of DE genes from edgeR_robust. c True set of DE genes from DESeq_glm. d True set of DE genes from DESeq2. e True set of DE genes from EBSeq. f True set of DE genes from Voom.