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. 2020 Mar 25;10:5442. doi: 10.1038/s41598-020-62403-2

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© The Author(s) 2020

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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Summary of methods. In comparing IR imaging to gene expression profiling, we took a multi-step approach. Imaging was followed by an automated analysis of spectral data for each pixel using machine learning. From the assignment of cellular and acellular identity of all pixels in the image, collagen dominant pixels were analyzed. The spectral properties of these pixels were related to a label (ECM3, non-ECM3) for further analysis.