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. 2023 Apr 26;13:6821. doi: 10.1038/s41598-023-32293-1

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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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

PMC Copyright notice

AI Autoencoder was more effective than non-AI tools for cluster analysis of cardiomyocyte scRNAseq data from mouse hearts. Cluster analysis of cardiomyocyte scRNAseq data was conducted via (A) AI Autoencoder (clusters ^AICMa-^AICMe), (B) Seurat (cluster ^SCMa-^SCMe), (C) ScanPY (clusters ^ScPYCMa-^ScPYCMf), or (D) scDHA (cluster ^scDHACMa-^scDHACMc) and displayed via (Row i) UMAP for identification of cardiomyocyte subpopulations. (Row ii) The proportion of cardiomyocytes from each cluster is displayed for each injury group and time point. (Rows iii-vii) The expression of (iii) Tnni1, (iv) Mki67, (v) Ccnb1, (vi) Xirp2, and (vii) Cd44 was quantified for each cardiomyocyte cluster. Similarities between cluster labels are coincidental (e.g., clusters ^AICMa, ^SCMa, and ^ScPYCMa do not represent the same subpopulation). Expression data were normalized as in Seurat, briefly: the raw counts were logarithm (base 2) transformed and scaled according to the total of UMIs and detected genes per cell.