2-AIN-505, 2-AIN-251: Seminar in Bioinformatics (1), (3)
Winter 2021

Xuran Wang, Jihwan Park, Katalin Susztak, Nancy R. Zhang, Mingyao Li. Bulk tissue cell type deconvolution with multi-subject single-cell expressionreference. Nat Commun, 10(1):380. 2019.

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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.