Cellular Composition Analysis

This guide contains a brief discription of MuSiC algorithm used within Dprofiler for estimating cellular compositions of Reference bulk expression data set using the scRNA expression data.

MuSiC Algorithm

The MuSIC algorithm employs single cell genomic expression profiles to acquire non-negative least squares estimates (Wang et al.). A specific feature of MUSIC allows the proportions of closely related cell types to be correctly estimated. To deal with collinearity, MuSiC employs a tree-guided procedure that recursively zooms in on closely related cell types. Rather than pre-selecting marker genes from scRNA-seq based only on mean expression, MuSIC gives weight to each gene allowing for the use of a larger set of genes in deconvolution. The weighting scheme prioritizes consistent genes across subjects: (i) up-weighing genes with low cross-subject variance (informative genes) and (ii) down-weighing genes with high cross-subject variance (non-informative genes). This requirement on cross-subject consistency is critical for transferring cell type-specific gene expression information from one dataset to another.