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

TLDR

Knowledge of cell‑type composition in disease‑relevant tissues is essential for identifying cellular disease targets. MuSiC uses single‑cell RNA‑seq gene expression to estimate cell‑type proportions in bulk RNA‑seq data, enabling researchers to leverage abundant bulk datasets to uncover disease‑related cellular contributions. MuSiC weights genes with cross‑subject and cross‑cell consistency to transfer cell‑type expression signatures from single‑cell data to bulk samples. MuSiC outperforms existing methods on pancreatic islet and kidney data across species and facilitates detailed cellular heterogeneity analysis, enabling disease‑mechanism insights from readily available bulk RNA‑seq.

Abstract

Abstract Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. We present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. By appropriate weighting of genes showing cross-subject and cross-cell consistency, MuSiC enables the transfer of cell type-specific gene expression information from one dataset to another. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables the characterization of cellular heterogeneity of complex tissues for understanding of disease mechanisms. As bulk tissue data are more easily accessible than single-cell RNA-seq, MuSiC allows the utilization of the vast amounts of disease relevant bulk tissue RNA-seq data for elucidating cell type contributions in disease.