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. 2023 Mar 21;14:1570. doi: 10.1038/s41467-023-37126-3

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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 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. 7 — a CPU times (log-scale) of DE workflows taken for analyzing lung adenocarcinoma (LUAD) epithelial cell (n = 7728) and COVID-19 monocyte (n = 100361) scRNA-seq data, each containing 10278 and 7242 genes, respectively. b Clustering heatmap of 46 DE workflows for LUAD epithelial cell data and four DE methods for TCGA LUAD data. The Spearman rank correlation of DE genes were used as the similarity measure. c Comparison of the capability of prioritizing disease-related genes between 46 DE workflows and their performance classified in terms of false positive/discovery controls, sign preservation of DE genes, speed, and scalability.