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. 2017 Mar 14;18(Suppl 2):142. doi: 10.1186/s12864-017-3490-3

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© The Author(s). 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 4 — a 3D-Principal Component Analysis: Class effects dominate in the simulated data, given the removal of PC1 (Two examples are shown: D2.2.301 and D2.2.302). b Heatmaps and hierarchical clustering (HCL): The remaining PCs may also be used as individual variables for clustering, and provide strong discrimination between classes D and D*. c Combining two datasets with different batch effects: Datasets A and B have the same differential feature set but different batch effects. Combining these followed by analysis of all principal components (PC) shows batch effects dominate. However, removal of PC1 perfectly recovers class-effect discrimination without having to perform any feature selection (Notation: A/B_D/D*_1/2 refers to the dataset, class and batches respectively)