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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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Schematic illustration of expression-to-expression modeling, cross-validation and choice of test and training samples. (a) The goal is to predict expression of a gene G in conditions (i.e., samples) C₁, C₂, …, C_m using the expression of transcription factors TF₁, TF₂, …, TF_n in the same conditions. (b) The schematic view of dividing conditions into training and test sets in cross-validation. A model is trained using the expression of TFs and gene G on the training set and is used to predict the gene expression in the test conditions. The predicted gene expression values (Ê) are compared to the real gene expression values (E) to provide a measure for the accuracy of the model. (c,d) The illustration of different ways of dividing conditions into test and training sets. In this toy example, only two TFs are considered and each point represents a condition. In (c) test points are located close to training points, but in (d) test points are far from training points. Evaluating the performance of a model such as the one shown in (a) using the partitions shown in (c) or (d) may lead to different results.