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. 2020 Sep 22;11:4779. doi: 10.1038/s41467-020-18618-y

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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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Fig. 5 — a Plot of the sensitivity to detect lymphoid and myeloid specific DMCTs (y-axis) vs. the smoking effect size (x-axis). Data points represent the mean sensitivity, as obtained over 10 different Monte-Carlo simulations, encompassing 482,077 CpGs of which 1000 are DMCTs. Darkblue/darkred datapoints are for n = 200 (100 cases and controls), darkgreen/purple are for n = 600 (300 cases and controls). Each sample is an in silico mixture of real DNAm profiles representing one purified CD4+ T-cell and one monocyte sample. For each case sample, 1000 DMCTs at the given average effect size in only one of the two cell-types was generated. The sensitivities for each of the ten Monte-Carlo runs are shown in skyblue/red. b As a, but now for the scenario where the DMCTs are introduced at the same CpG in both cell-types. Sensitivity to detect the DMCT in the lymphoid and myeloid lineage is shown. c, d As a, b, but now for the specificity. e, f As a, b, but now for the precision or positive predictive value (PPV). Specificity and PPV values for both choices of sample size are effectively identical.