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. 2018 Aug 3;8:11685. doi: 10.1038/s41598-018-29763-2

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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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Principal component analysis. Principal component analysis (PCA) is a statistical method that is typically used as an exploratory method for data analysis, as it provides insight in the quality of a dataset, and in differences between experimental groups. (A) First two principal components (PCs) of triplicate genome-wide expression profiles of Huh7.5 cells in FBS and 8, 15, and 23 days after transfer to HS. To determine the similarity in gene expression between HS-cultured cells and primary hepatocytes, published hepatocyte expression profiles were projected onto the PCA, by applying the gene weights associated with PC1 and PC2 to the gene expression levels (squares). (B) Scree-plot indicating the total variance explained by the individual PCs. 75% of variance in gene expression is explained by the first two PCs principal components.