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. 2023 Oct 2;20(10):1530–1536. doi: 10.1038/s41592-023-02007-6

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© The Author(s) 2023

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. 4 — a, Fluorescence intensities of Alexa568 (PV marker E-cadherin) and Alexa647 (CV marker Glul), with percentages in indicated bins. Each dot represents one shape. b, Intensities of the spatial markers as in a across eight spatial bins. The boxes are first and third quartiles, the thick line is the median, whiskers are ±1.5 interquartile range and outliers are indicated as individual points. c, Confusion matrix of a ML model with five classes, informed by microscopy and proteomics data. Colors scale with counts in each box. d, Predicted classes of segmented hepatocytes. The hue is the maximum class probability. e, Density plot of predicted versus measured intensities of a section excluded from machine learning (R = 0.78). f, Spatial depiction of one biological replicate with microscopy ground truth on top right, and three predictions. Orange, E-cadherin; red: Glul; green: WGA. *Sectioning artifact. Scale bar, 150 μm.