PT  - JOURNAL ARTICLE
AU  - Tinyi Chu
AU  - Charles G. Danko
TI  - Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
AID  - 10.1101/2020.01.07.897900
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.07.897900
4099  - http://biorxiv.org/content/early/2020/08/19/2020.01.07.897900.short
4100  - http://biorxiv.org/content/early/2020/08/19/2020.01.07.897900.full
AB  - Understanding the complicated 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 tumor state and stromal 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.