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. 2021 Apr 23;8:115. doi: 10.1038/s41597-021-00890-2

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© The Author(s) 2021

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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Fig. 7 — Principal component analysis. A complete matrix across six cell lines (A549, HCT116, HT29, MCF7, RKO and SW620) was used for PCA analysis. This included 2,914 protein expression values. The first two principal components explaining 35% and 30% of the variance are displayed. The colour of the points indicates the sample types whereas the shape of the point indicates the corresponding study. Panel a) shows the PCA space after “ppb” normalisation. It can be observed that points (individual samples) cluster according to the study of origin. Panel b) shows the PCA space after limma batch effects removal. It can be observed that cell lines cluster together based on the lineage rather than based on the study of origin.