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. 2018 Apr 26;8:6620. doi: 10.1038/s41598-018-24937-4

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© The Author(s) 2018

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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Systematic generation of cross-validation partitions with gradually increasing distinctness score using simulated annealing. (a) The distribution of distinctness scores of 200 conditions in each CV partition generated using simulated annealing. The dashed and dotted lines show the average distinctness score of the 10 CCV and the 10 RCV partition collections, respectively. (b) The distribution of the test set prediction accuracies for 500 genes. The prediction accuracy is measured using the PCC of model-predicted expression values and real expression values. (c) The accuracy of predicting test set expression of one gene for the 30 CV partitions versus the distinctness score of the CV partitions. (d) The histogram of Spearman’s rank correlation coefficient between the prediction accuracy and average distinctness value, across the 30 CV partitions. The histogram includes one correlation value for each of the 500 genes. Each correlation value is calculated as shown in Fig. 5c.