Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Tinyi Chu, View ORCID ProfileCharles G. Danko
doi: https://doi.org/10.1101/2020.01.07.897900
Tinyi Chu
1Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
2Graduate field of Computational Biology, Cornell University, Ithaca, NY 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: dankoc@gmail.com tc532@cornell.edu
Charles G. Danko
1Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
3Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Charles G. Danko
  • For correspondence: dankoc@gmail.com tc532@cornell.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

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 Statement

The authors have declared no competing interest.

Footnotes

  • We have made several changes following a rigrous peer review: To emphasize the general utility of our tool beyond cancer, we renamed it to BayesPrism (it was called TED in our first submission). We bolster the general utility of BayesPrism using substantial new benchmarks that go far beyond those included in our initial submission. We have also re-written a much more extensive description of how BayesPrism works in the revised manuscript, which we believe should clarify misconceptions that reviewers had. Finally, we have substantially revised our analysis of bulk cancer genomic RNA-seq data to emphasize results that we believe will have the largest impact.

Copyright 
The copyright holder has placed this preprint in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors.
Back to top
PreviousNext
Posted August 19, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
Tinyi Chu, Charles G. Danko
bioRxiv 2020.01.07.897900; doi: https://doi.org/10.1101/2020.01.07.897900
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
Tinyi Chu, Charles G. Danko
bioRxiv 2020.01.07.897900; doi: https://doi.org/10.1101/2020.01.07.897900

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (2646)
  • Biochemistry (5266)
  • Bioengineering (3678)
  • Bioinformatics (15796)
  • Biophysics (7253)
  • Cancer Biology (5627)
  • Cell Biology (8095)
  • Clinical Trials (138)
  • Developmental Biology (4765)
  • Ecology (7516)
  • Epidemiology (2059)
  • Evolutionary Biology (10576)
  • Genetics (7730)
  • Genomics (10131)
  • Immunology (5193)
  • Microbiology (13905)
  • Molecular Biology (5385)
  • Neuroscience (30779)
  • Paleontology (215)
  • Pathology (879)
  • Pharmacology and Toxicology (1524)
  • Physiology (2254)
  • Plant Biology (5022)
  • Scientific Communication and Education (1041)
  • Synthetic Biology (1385)
  • Systems Biology (4146)
  • Zoology (812)