Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach

An-Shun Tai, Chun-Chao Wang and Wen-Ping Hsieh ()
Additional contact information
An-Shun Tai: National Tsing Hua University
Chun-Chao Wang: National Tsing Hua University
Wen-Ping Hsieh: National Tsing Hua University

Statistics in Biosciences, 2023, vol. 15, issue 3, No 9, 692-718

Abstract: Abstract Interest in studying genomics and transcriptomics at the single-cell level has been increasing. One of the keys to single-cell study is developing cell-sorting technology to separate cells according to their type. However, the process of cell isolation changes the cell microenvironment that affects gene activity, and this change in gene expression can affect the conclusion of the single-cell study. To address this, we propose a novel PEnalized deconvolution Analysis for Cell separation-induced Heterogeneity (PEACH). By adopting a Bayesian variable selection scheme, PEACH can simultaneously decompose cell-type-specific expression from bulk tissue and identify cell separation-induced differential expression (CSI-DE) genes. We validated PEACH by using four benchmark datasets and one in silico mixture dataset. In the real application, we used PEACH to analyze an immune-related disease dataset, a blood dataset, and a skin dataset, and we consistently identified immediate-early genes, ribosomal protein genes, and mitochondrial genes across the three datasets. Our study illustrates that genes sensitive to the cell-sorting process are biologically meaningful and nonnegligible, and it may provide new insights into single-cell studies for transcriptomic analysis. The model has been implemented in the R package “PEACH,” and the algorithm is available for download.

Keywords: Gene expression deconvolution; Cell separation-induced differential expression; Bayesian variable selection (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-022-09344-8 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:15:y:2023:i:3:d:10.1007_s12561-022-09344-8

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

DOI: 10.1007/s12561-022-09344-8

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 ().