A Statistical Method for Association Analysis of Cell Type Compositions

Licai Huang, Paul Little, Jeroen R. Huyghe, Qian Shi, Tabitha A. Harrison, Greg Yothers, Thomas J. George, Ulrike Peters, Andrew T. Chan, Polly A. Newcomb () and Wei Sun ()
Additional contact information
Licai Huang: Fred Hutchinson Cancer Research Center
Paul Little: Fred Hutchinson Cancer Research Center
Jeroen R. Huyghe: Fred Hutchinson Cancer Research Center
Qian Shi: Mayo Clinic
Tabitha A. Harrison: Fred Hutchinson Cancer Research Center
Greg Yothers: University of Pittsburgh
Thomas J. George: University of Florida Health Cancer Center
Ulrike Peters: Fred Hutchinson Cancer Research Center
Andrew T. Chan: Massachusetts General Hospital and Harvard Medical School
Polly A. Newcomb: Fred Hutchinson Cancer Research Center
Wei Sun: Fred Hutchinson Cancer Research Center

Statistics in Biosciences, 2021, vol. 13, issue 3, No 1, 373-385

Abstract: Abstract Gene expression data are often collected from tissue samples that are composed of multiple cell types. Studies of cell type composition based on gene expression data from tissue samples have recently attracted increasing research interest and led to new method development for cell type composition estimation. This new information on cell type composition can be associated with individual characteristics (e.g., genetic variants) or clinical outcomes (e.g., survival time). Such association analysis can be conducted for each cell type separately followed by multiple testing correction. An alternative approach is to evaluate this association using the composition of all the cell types, thus aggregating association signals across cell types. A key challenge of this approach is to account for the dependence across cell types. We propose a new method to quantify the distances between cell types while accounting for their dependencies, and use this information for association analysis. We demonstrate our method in two applied examples: to assess the association between immune cell type composition in tumor samples of colorectal cancer patients versus survival time and SNP genotypes. We found immune cell composition has prognostic value, and our distance metric leads to more accurate survival time prediction than other distance metrics that ignore cell type dependencies. In addition, survival time-associated SNPs are enriched among the SNPs associated with immune cell composition.

Keywords: Cell type composition; Genome-wide associations; Survival time (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-020-09293-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:13:y:2021:i:3:d:10.1007_s12561-020-09293-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-020-09293-0

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().