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. 2019 Mar 14;20:55. doi: 10.1186/s13059-019-1664-9

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© The Author(s). 2019

Open AccessThis 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. 4 — Dissection of DNA methylation heterogeneity in Ewing sarcoma samples profiled using RRBS. a Principal component analysis of an RRBS dataset for Ewing sarcoma tumors, cell lines, and mesenchymal stem cells, based on DNA methylation values aggregated across Ensembl Regulatory Build regions. b Density scatterplot comparing the aggregated DNA methylation levels between Ewing sarcoma tumors (N = 140) and Ewing sarcoma cell lines (N = 16). Marked in purple are the most highly ranking differentially methylated regions up to an automatically selected rank cutoff. c Density scatterplot comparing DNA methylation variance between Ewing sarcoma tumors and cell lines. Significant differentially variable regions are marked in brown. d Enrichment (log-odds ratios) based on LOLA analysis for the differentially methylated regions shown in panel b and in panel e. Differently colored bars represent different types of genomic region data. e Density scatterplot comparing the log-ratios between DNA methylation levels and variance in Ewing sarcoma tumors and cell lines. f Enrichment (log-odds ratios) based on LOLA analysis for differentially variable regions shown in panel c and in panel e. ESC, embryonic stem cell; CGIs, CpG islands