Inferring cellular heterogeneity with mixture models for DNA methylation rates

Abstract

Cellular heterogeneity is a hallmark of biological tissues and plays a central role in disease progression, diagnosis, and prognosis. Yet, accurately characterizing this heterogeneity from bulk molecular profiles remains challenging because observed signals arise from mixtures of multiple cell populations. Cell deconvolution aim to recover the relative abundance of constituent cell types from such heterogeneous measurements, but most existing approaches implicitly rely on restrictive assumptions on residual errors, including independence, homoscedasticity, and normality. These assumptions are rarely satisfied in omics data, which are inherently bounded and overdispersed. In this work, we show that whole-genome cell-type specific DNA methylation profiles exhibit latent group structures that can substantially impair deconvolution accuracy when ignored. We therefore propose a mixture of non-negative Beta regression models estimated through an Expectation-Maximization algorithm for DNA methylation rates. Our framework naturally incorporates a feature selection mechanism through mixture component identification, making component selection a critical step of the inference procedure. We further propose a dedicated criterion for component selection and assess the performance of the approach through an extensive comparative study across several in vitro benchmark datasets. Our results demonstrate that deconvolution accuracy is highly sensitive to latent component structure and show that explicitly modeling this heterogeneity yields substantial improvements over standard whole-genome deconvolution strategies. Altogether, this work establishes mixture modeling of DNA methylation data as a powerful new direction for robust and accurate cell deconvolution.