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. 2022 Jul 12;23:276. doi: 10.1186/s12859-022-04817-5

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© The Author(s) 2022

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — Ranking of markers and computational time for all the evaluated methods: Distribution of the ranks of true markers for the MCA-Spleen (A), MCA-Lung (B), Human-Lung &PBMC (C), Human-BM (bone marrow CITE-seq data) (D), and Human-PBMC (human peripheral blood mononuclear cells CITE-seq data) (E) data sets for all evaluated methods. For simplicity, the distributions and ranks of all the cell types were combined for a given method and data set. The methods that gave lower ranks for the true markers were the best in recovering the true cell markers. (F) Distribution of computational time for all the evaluated methods in seconds. Methods are ranked by increasing median value and values are shown in log 10 scale. The statistical significance of the best overall method (sc2marker + DB) compared with the other computing method is indicated as; * p-value < 0.1, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001 by the Wilcoxon rank sum test (adjusted using the Bonferroni correction)