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. 2019 Dec 16;10:5734. doi: 10.1038/s41467-019-13418-5

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© The Author(s) 2019

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 Schematic illustration of the workflow used to phenotype B cell populations and identify subpopulations. b Quantified surface protein distribution of developing B cell populations visualised by upset plot. The bar chart on top visualises the number of proteins contained within each intersection as defined in the lower part. c Heatmap of z-score abundance values. Surface proteins used for subsequent flow cytometry analysis are indicated to the right. d Correlation analysis (spearman’s rho) comparing autoCSC and RNA-seq with flow cytometry. e, f Box-plots showing autoCSC obtained surface abundance values for (e) CD20 and (f) CD180 in B cell populations. The center line of box-plots represents the median, box limits the upper and lower quartiles, whiskers the 1.5x interquartile range and dots any outlier data points. g Flow cytometry distributions of CD20 and CD180 surface abundances during B cell development.