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. 2021 Nov 22;22(12):1577–1589. doi: 10.1038/s41590-021-01059-0

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© The Author(s) 2021

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. 2 — a, For each surface marker measured in our 97-plex Abseq data, the fraction of variance explained by different covariates (colored insets in top row) is displayed. For this, every single cell from healthy young individuals (n = 3 samples, 28,031 single cells) was assigned to a cell type identity (blue inset, see Fig. 1b), and cytotoxicity, stemness and cell cycle scores (red inset, see Extended Data Fig. 5e) as well as technical covariate scores were determined. Additionally, pseudotime analyses were used to assign differentiation scores to HSPCs (orange inset, see Fig. 3a). These covariates were then used to model surface marker expression in a linear model. The fraction of variance explained by each of the processes was quantified. See Methods, section Modeling variance in surface marker expression for details. b, Cell type identity markers. Dot plot depicting the expression of the 25 surface markers with the highest fraction of variance explained by cell type across main populations. Colors indicate mean normalized expression, point size indicates the fraction of cells positive for the marker. Automatic thresholding was used to identify positive cells, see Methods, section Thresholding of surface marker expression for details. c, T cell subtype markers. The expression of the 20 surface markers with the highest fraction of variance explained by T cell subtype is displayed, legend as in b. mem, memory; tissue-r, tissue-resident. d, HSPC differentiation markers. Megakar, megakaryocytic. Dot plot depicting expression changes of markers across pseudotime in CD34⁺ HSPCs. Color indicates logarithmic fold change (FC) between the start and the end of each pseudotime trajectory. Point size indicates the mutual information in natural units of information between pseudotime and marker expression. The 25 surface markers with the highest fraction of variance explained by pseudotime covariates are displayed.