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. 2022 Mar 8;13:1205. doi: 10.1038/s41467-022-28845-0

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© The Author(s) 2022

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 Coverage comparison between 3 TNBC 10x scRNA-seq datasets and fibroblast Smart-seq2 dataset. UMI-based scRNA-seq shows uneven distribution of reads across the mitochondrial genome while Smart-seq2 is generally more well-covered. b Clonal assignment and mean allelic ratio of 1097 cells from TNBC1. c Confusion matrix between CNV subclones and confidently assigned mitochondrial clones. d UMAP plot with tumor cells only. Mitochondrial clone 1 highly overlaps with CNV clone B. e Volcano plot showing negative log10 P-values (Seurat FindMarkers with DESeq2 model; two-sided Wald test) against average log2 fold changes (FC) for DE between T0 (Tumor & MT0) and T1 (Tumor & MT1). Significant DE genes (Adjusted p-value < 0.01) highlighted in red. DE genes that are associated with breast cancer are labeled.