PT  - JOURNAL ARTICLE
AU  - Chu, Tinyi
AU  - Wang, Zhong
AU  - Pe’er, Dana
AU  - Danko, Charles G.
TI  - Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
AID  - 10.1101/2020.01.07.897900
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.01.07.897900
4099  - http://biorxiv.org/content/early/2021/11/13/2020.01.07.897900.short
4100  - http://biorxiv.org/content/early/2021/11/13/2020.01.07.897900.full
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.