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. 2021 Mar 2;22:102. doi: 10.1186/s12859-021-04040-8

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© The Author(s) 2021

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 2 — Co-clustering between genes highly correlated with LP-184 sensitivity in training set and cell lines subgroups. a This heat map displays the clustering pattern across the top 5 cell lines known to be sensitive and resistant to LP-184 (from actual IC50 in NCI-60 data) arranged in vertical columns and the top 20 correlated genes (10 positively correlated and 10 negatively correlated) with actual known LP-184 sensitivity arranged in horizontal rows. b This heat map displays the clustering pattern across the top 25 cell lines predicted sensitive and resistant to LP-184 (from predicted IC50 in CCLE testing data) arranged in vertical columns and the top 20 correlated genes (10 positively correlated and 10 negatively correlated) with actual known LP-184 sensitivity arranged in horizontal rows