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. 2018 Jul 18;8:10885. doi: 10.1038/s41598-018-29077-3

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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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Workflow for global and targeted network analyses. One microarray dataset and a RNA-seq dataset prepared according to 7 normalization procedures were used to generate eight expression matrices analyzed with six different distance measurements (Pearson’s or Spearman’s Correlation Coefficient, unranked or ranked with HRR, Mutual Information (MI) or Partial Correlations (PC)) to obtain 48 distance matrices. Each of these matrices was thresholded to obtain global networks at different confidence thresholds. Global networks were evaluated and also queried with specific guide gene sets reflecting 5 different pathways in a process named Pathway Level Correlation (PLC). The resulting subnetworks were evaluated and used to construct co-ocurrence networks between microarray and RNA-seq datasets. In white are indicated the figures corresponding to the different steps analyzed. Dataset × distance combinations are indicated in blue and characteristics that are improved by these combinations.