Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology Journal Article


Authors: Chu, T.; Wang, Z.; Pe’er, D.; Danko, C. G.
Article Title: Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology
Abstract: Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.
Keywords: sequence analysis; genetics; melanoma; bayes theorem; gene expression; skin neoplasms; endothelium cell; endothelial cells; skin tumor; head and neck neoplasms; sequence analysis, rna; head and neck tumor; single cell analysis; single-cell analysis; procedures; humans; human
Journal Title: Nature Cancer
Volume: 3
Issue: 4
ISSN: 2662-1347
Publisher: Nature Research  
Date Published: 2022-04-01
Start Page: 505
End Page: 517
Language: English
DOI: 10.1038/s43018-022-00356-3
PUBMED: 35469013
PROVIDER: scopus
PMCID: PMC9046084
DOI/URL:
Notes: PDF incorrectly formats the author Tin Yi Chu’s name -- Source: Scopus
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  1. Dana Pe'er
    110 Pe'er
  2. Tin Yi Chu
    7 Chu