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

SUBMITTER: Cai M 

PROVIDER: S-EPMC9991889 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature