Fig. 7: Classification of cells from COVID-19 clinical samples to disease-informative (or non-informative).
From: SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data
a UMAP visualizations of all cells in the SiFTed data colored by association to disease-informative and non-informative clusters. Leiden clusters in the SiFTed data were classified according to cluster-purity score (“Methods”). Insets show a zoom-in on the cDC (left) and Platelet (right) subpopulations. b Enrichment analysis of the differentially expressed genes in the disease-informative cluster (compared to non-informative, using top 50 genes). The size of the circles indicates the number of genes. Color indicates the magnitude of \(-{\log }_{10}\left({p}_{{adj}}\right)\). \({p}_{{adj}}\) is calculated using the hypergeometric distribution, a one-sided version of Fisher’s exact test, with Benjamini–Hochberg correction. c Disease-informative bar plot of the proportion of cell populations, separated into disease-informative, disease non-informative, and healthy (according to the assignment shown in (a)). Cell types are sorted according to the fraction of disease cells. Source data of (b, c) are provided as a Source Data file.
