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. 2022 Mar 23;5:255. doi: 10.1038/s42003-022-03218-x

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© The Author(s) 2022

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. 2 — a Cells are compared using initial naive metrics such as mean EGFR intensity and cell size to show the difficulty separating the cell populations. b–g The model architectures are quantitatively evaluated using cluster purity and normalized mutual information (k-means with the number of clusters = 2). The sample size for all comparison methods and metrics is n = 15,898 single-cell images. A qualitative comparison is made using visual separation of two labeled cell populations in UMAP embedding space and visual analysis of cells from UMAP regions to identify biologically distinct factors. Rotation angles of cells are shown in UMAP embedding to show the influence of unimportant features on downstream analysis. h The same population of cells is compared using radial slope (a metric inferred from visually analyzing the regional cell images in g). Scale bars in b–g represent 20 μm.