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. 2023 Sep 5;14:5400. doi: 10.1038/s41467-023-41228-3

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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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Fig. 4 — a Schematics showing the idea of RePACT. A trajectory-based method can improve the sensitivity of disease gene identification if a disease-associated β-cell heterogeneity exists. b Plot all β-cells from the scRNA-seq data in the top 3 principal components spaces. Blue or red color indicates whether the cells are from healthy or T2D donors. c After building a β-cell T2D trajectory with RePACT using PC1-PC10 (Methods), the violin plot (median ± upper and lower quartiles) compares the distribution of T2D pseudo-index for β-cells from each donor. Density plot (top) compares all β-cells from healthy (n = 7) or T2D donors (n = 4) with p-value (two-sided Kolmogorov-Smirnov test). d Left heatmap: the expression changes of the top up/downregulated genes along the T2D trajectory. Right heatmap: the expression changes of the same genes in a previously published cohort of β-cells. e reproducibility of intra-donor or inter-donor heterogeneity genes from this study compared to previously published study. Color intensity indicates the odds ratio. P values (two-sided Fisher’s exact test) and the numbers of genes are shown in the squares. f–i Examples of T2D trajectory genes with intra- and inter-donor heterogeneity. Each violin plot still shows the distribution of T2D index of β-cells in all donors in the same way as (c) but with additional information on the expression of denoted gene. In every violin, cells are binned into 4 subpopulation-quartiles according to the T2D index. Normalized gene expression is visualized in each violin quartile. Color scale indicates averaged expression level. Examples of four categories of genes are: f T2D-downregulated intra-donor heterogeneous genes; g T2D-downregulated inter-donor heterogeneous genes; h T2D-up-regulated intra-donor heterogeneous genes; i T2D-up-regulated inter-donor heterogeneous genes. j Ranking genes based on if they show consistent intra-donor heterogeneity. Y-axis indicates the significance from Fisher’s method after integrating p-values (two-sided t-test) from RePACT analyses of every individual donor using T2D trajectory (Methods). T2D-down intra-donor heterogeneous genes (n = 97); T2D-down inter-donor heterogeneous genes (n = 110); T2D-up intra-donor heterogeneous genes (n = 161); T2D-up inter-donor heterogeneous genes (n = 351). Dash lines indicate a cutoff of 2 (integrated p-value < 0.01) to classify the genes, examples in (f–i). k GSEA analysis for the four categories of T2D trajectory genes. Total number of genes of each function term is shown by each row, and the gene number in a tested category is shown in the square.