Bulk tissue cell type deconvolution with multi-subject single-cell expression reference
By Xuran Wang, Jihwan Park, K. Suszták, Nancy R. Zhang, Mingyao Li. Nature Communications, 2018
796 citations recorded by the source corpus.
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. Bulk tissue RNA-seq data reveals transcriptomic profiles but masks the contributions of different cell types. Here, the authors develop a new method for estimating cell type proportions from bulk tissue RNA-seq data guided by multi-subject single-cell expression reference.
Subfields: Molecular Biology, Oncology, Surgery
View Paper: https://www.semanticscholar.org/paper/45da446d45e4dd08c1b04b680a025faa0b7931de
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
Citations in the corpus
- Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, background): “19 and quantified the deconvolution performance by computing the root mean square error (r.m.s.e.) between the estimated cell-type composition and the underlying truth on each location.”
- Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis (2020, methodology): “Deconvolution was performed with the R package MuSiC (42, 43).”