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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/.

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AI Autoencoder was more effective than non-AI tools for cluster analysis of scRNAseq data from mouse hearts. Cluster analysis of mouse heart scRNAseq data was conducted via (A) AI Autoencoder, (B) Seurat, (C) ScanPY, (D) SC3, (E) CIDR, (F) RaceID, (G) scDHA, (H) ssCCCEES or (I) DCA and (Column i) displayed via UMAP for identification of cell-type specific clusters. (Column ii) Myh7 and (Column iii) Ryr2 expression was quantified across the corresponding UMAP and presented as a heat map.

AI Autoencoder was more effective than non-AI tools for cluster analysis of scRNAseq data from mouse hearts. Cluster analysis of mouse heart scRNAseq data was conducted via (A) AI Autoencoder, (B) Seurat, (C) ScanPY, (D) SC3, (E) CIDR, (F) RaceID, (G) scDHA, (H) ssCCCEES or (I) DCA and (Column i) displayed via UMAP for identification of cell-type specific clusters. (Column ii) Myh7 and (Column iii) Ryr2 expression was quantified across the corresponding UMAP and presented as a heat map.