EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data
Pub. online: 26 February 2025
Type: Statistical Data Science
Open Access
Open Access
†
These authors contributed equally to this work.
Received
1 October 2024
1 October 2024
Accepted
24 January 2025
24 January 2025
Published
26 February 2025
26 February 2025
Abstract
Cellular deconvolution is a key approach to deciphering the complex cellular makeup of tissues by inferring the composition of cell types from bulk data. Traditionally, deconvolution methods have focused on a single molecular modality, relying either on RNA sequencing (RNA-seq) to capture gene expression or on DNA methylation (DNAm) to reveal epigenetic profiles. While these single-modality approaches have provided important insights, they often lack the depth needed to fully understand the intricacies of cellular compositions, especially in complex tissues. To address these limitations, we introduce EMixed, a versatile framework designed for both single-modality and multi-omics cellular deconvolution. EMixed models raw RNA counts and DNAm counts or frequencies via allocation models that assign RNA transcripts and DNAm reads to cell types, and uses an expectation-maximization (EM) algorithm to estimate parameters. Benchmarking results demonstrate that EMixed significantly outperforms existing methods across both single-modality and multi-modality applications, underscoring the broad utility of this approach in enhancing our understanding of cellular heterogeneity.
Supplementary material
Supplementary MaterialR package EMixed is publicly hosted on GitHub (https://github.com/manqicai/EMixed)
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