Paper Info
Title
Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution
Abstract
Motivation: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. Results: To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data.
Year
DOI
Venue
2022
10.1093/bioinformatics/btac279
BIOINFORMATICS
DocType
Volume
Issue
Journal
38
11
ISSN
Citations 
PageRank 
1367-4803
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Manqi Cai100.34
Molin Yue200.34
Tianmeng Chen300.34
Jinling Liu400.34
Erick Forno500.34
Xinhua Lu600.34
Timothy R Billiar700.68
Juan Celedón800.34
Chris McKennan900.34
Wei Chen1010438.74
Jiebiao Wang1100.34