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. 2020 Jul 30;10:12900. doi: 10.1038/s41598-020-68858-7

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© The Author(s) 2020, corrected publication 2024

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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Projection into first two principal components after PCA of feature space (top row) and explanation space (bottom row) of Synthetic, Fashion-MNIST, UK Biobank Brain-Age, and Cancer Genome Atlas datasets. Shown are the (sub-)classes of the three-class superset used for binary classification. As can be seen, clusters are well defined in explanation space; for biomedical datasets, these sub classes correspond to real subtypes. For example in TCGA (rightmost plot), the visible clusters correspond to Wound Healing (black), IFN-gamma Dominant (yellow), and Inflammatory (pink) cancer tissue immune model types.