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Journal: Communications Biology
Article Title: A realistic FastQ-based framework FastQDesign for ScRNA-seq study design issues
doi: 10.1038/s42003-025-07938-8
Figure Lengend Snippet: a–c The UMAPs of the FastQ sample with 10% read depth of all cells, the UMI sample with 10% read depth of all cells, 10% cell and total read depth, were colored with the consistency of cell clustering match with the reference. d–f The pairwise minimal p-value 's of each gene among the clusters from the samples and the reference, x is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-\log 10(p\_val\_adj)$$\end{document} − log 10 ( p _ v a l _ a d j ) in the reference, y is the same value in the sample, only the significant DE genes in either reference or samples are shown. g–i The pairwise minimal p-value 's of each gene between the condition within the cluster from the samples and the reference, only the significant DE genes in either reference or samples are shown. j, k The impact of varying read depth, and cell numbers on ARI and Jaccard indexes, 10 simulations were conducted at each setting.
Article Snippet: The overall
Techniques: