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Fig. 5 | Genome Biology

Fig. 5

From: SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3′ tag-based RNA-seq of single cells

Fig. 5

Identifying altered PAS usages upon SARS-CoV-2 infection. a Less proximal PAS usage in monocytes than other immune cell types, such as DCs, in six PBMC scRNA-seq datasets from 10x Genomics. b Schematic of APA analysis by SCAPTURE to compare PAS usage changes between healthy individuals and severe COVID-19 patients with SARS-CoV-2 infection at single-cell level. See “Methods” section for details. c UMAP plots showing DTE-based single-cell clustering of PBMC scRNA-seq datasets from healthy individuals and COVID-19 patients by SCAPTURE. Left, single cells labeled with disease group. Right, single cells labeled with annotated cell type. d UMAP plots showing global preference of proximal PAS usage in healthy individuals and COVID-19 patients at single-cell level. “Methods” section for details. e Proximal PAS usage in immune cell types from PBMC scRNA-seq datasets of healthy individuals (left) and COVID-19 (right) patients. f Genes with altered PAS usage between monocytes and DCs. Examples of known genes with reported functions, such as activation of innate/adaptive immune response, antigen processing/presentation via MHC, or dendritic spine development, were shown. g Global preference of proximal PAS usage in different immune cell types from COVID-19 samples. Statistical significance was assessed by Kolmogorov-Smirnov test. h Preferential proximal PAS usage of some immunoglobulin genes in B cells (left) and plasma cells (right) of COVID-19 samples. Statistical significance was assessed with Seurat (“Methods” section)

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