RT Journal Article
SR Electronic
T1 Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.07.897900
DO 10.1101/2020.01.07.897900
A1 Chu, Tinyi
A1 Wang, Zhong
A1 Pe’er, Dana
A1 Danko, Charles G.
YR 2021
UL http://biorxiv.org/content/early/2021/11/13/2020.01.07.897900.abstract
AB Understanding the interactions between cells in their environment is a major challenge in genomics. Here we developed BayesPrism, a Bayesian method to jointly predict cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 1,412 bulk RNA-seq samples in primary glioblastoma, head and neck squamous cell carcinoma, and melanoma using single-cell datasets of 85 patients. We identified cell types correlated with clinical outcomes and explored spatial heterogeneity in malignant cell states and non-malignant cell type composition. We refined subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T cells, fibroblasts, and endothelial cells across multiple tumor types. Our work introduces a new lens that uses scRNA-seq to accurately infer cellular composition and expression in large cohorts of bulk data.Competing Interest StatementThe authors have declared no competing interest.