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. 2019 Nov 4;20:543. doi: 10.1186/s12859-019-3163-0

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© The Author(s). 2019

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. 1 — Workflow designed to produce a pathway space representation of a gene expression data set. The expression values for all q_i genes in the i’th pathway (green columns) are extracted from the original (nxm) expression matrix to form a new (nxq_i) matrix. PCA is performed on this matrix and the score on the first component retained as the pathway score. This is repeated for each of the k pathways and the pathway scores assembled into a new ‘pathway space’ matrix of dimension (nxk) (orange)