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.
Recommended Citation
M. Cai and M. Yue and T. Chen and J. Liu and E. Forno and X. Lu and T. Billiar and J. Celedón and C. Mckennan and W. Chen and J. Wang, "Robust and Accurate Estimation of Cellular Fraction from Tissue Omics Data Via Ensemble Deconvolution," Bioinformatics, vol. 38, no. 11, pp. 3004 - 3010, Oxford University Press, Jun 2022.
The definitive version is available at https://doi.org/10.1093/bioinformatics/btac279
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
1460-2059; 1367-4803
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Oxford University Press, All rights reserved.
Publication Date
01 Jun 2022
PubMed ID
35438146
