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. 2023 Jan 14;14:223. doi: 10.1038/s41467-023-35923-4

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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. 5 — a TOSICA predicts cell types reliably across different cell types even when using healthy individuals as reference (left) and COVID19 patients as query (right). Colors denote 29 origin labels. Red circled cell types are unique in query. b Comparison of integration accuracy on query data places TOSICA first among 13 methods. Each score is minimum–maximum scaled between 0 and 1. Overall scores are computed using a 40:60-weighted mean of batch correction and bio-conservation scores. c, d TOSICA attention score based UMAP predicts 3 known (c) and 6 novel (d) monocyte types. e, f Subtype 3 monocytes increases (e) and subtype 4 decreases (f) in abundance from healthy (N = 25), to moderate (N = 79), and to severe (N = 91) COVID-19. Statistical test is two-sided. * RCC p < 0.05; ***p < 0.001. g TOSICA attention score of 6 transcription factors distinguishes subtype 3 and 4 monocytes across different states of COVID19. h The expression levels of major targets of the 6 TFs (g) generally show consistent trends with TFs attention score. Source data are provided as a Source data file.