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. 2016 Aug 23;6:32007. doi: 10.1038/srep32007

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Copyright © 2016, The Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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IPA was performed to analyze the SE-mRNA and SE-protein datasets as shown in Fig. 4. (a) Canonical pathway analysis shows most significant up- and down-regulated canonical pathways in SE cells compared to hiPSCs. Pearson’s correlation coefficient (top) and activation z-score (bottom) are shown. (b) Bio function analysis using IPA shows most significant up- and down-regulated bio-functions in SE cells compared to hiPSCs. Pearson’s correlation coefficient (top) and activation z-score (bottom) are shown. (c) Upstream Regulator Analysis in IPA is a tool that predicts upstream regulators from gene expression data based on the literature and compiled in the Ingenuity Knowledge Base. A Fisher’s Exact Test p-value is calculated to assess the significance of enrichment of the gene expression data for the genes downstream of an upstream regulator. X-axis and Y-axis showed the upstream regulators in SE vs. hiPSCs using cDNA microarray (SE-mRNA) and quantitative proteomics (SE-protein) datasets, respectively. Pearson’s correlation coefficient was calculated. (d) Mechanistic network analysis shows the activation of TGFβ superfamily, Wnt/β-catenin, and NF-κB signaling pathways in both cDNA microarray (left) and quantitative proteomics (right) datasets.