Fig. 1 | Nature Communications

Fig. 1

From: Bulk tissue cell type deconvolution with multi-subject single-cell expression reference

Fig. 1

Overview of MuSiC framework. MuSiC starts from scRNA-seq data from multiple subjects, classified into cell types (shown in different colors), and constructs a hierarchical clustering tree reflecting the similarity between cell types. Based on this tree, the user can determine the stages of recursive estimation and which cell types to group together at each stage. MuSiC then determines the group-consistent genes and calculates cross-subject mean (red to blue) and cross-subject variance (black to white) for these genes in each cell type. MuSiC up-weighs genes with low cross-subject variance and down-weighs genes with high cross-subject variance. In the example shown, deconvolution is performed in two stages, only cluster proportions are estimated for the first stage. Constrained by these cluster proportions, the second stage estimates cell type proportions, illustrated by the length of the bar with different colors. The deconvolved cell type proportions can then be compared across disease cohorts

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