Journal of Data Science

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.

Abstract: Methods used to detect differentially expressed genes in situations with one control and one treatment are t-tests. These methods do not per- form well when control and treatment variances are different. In situations with a control and more than one treatment, it is common to apply analysis of variance followed by a Tukey and/or Duncan test to identify which treat- ment caused the difference. We propose a Bayesian approach for multiple comparison analysis which is very useful in the context of DNA microarray experiments. It uses a priori Dirichlet process and Polya urn scheme. It is a unified procedure (for cases with one or more treatments) which detects differentially expressed genes and identify treatments causing the difference. We use simulations to verify the performance of the proposed method and compare it with usual methods. In cases with control and one treatment and control and more than one treatment followed by Tukey and Duncan tests, the method presents better performance when variances are different. The method is applied to two real data sets. In these cases, genes not detected by usual methods are identified by the proposed method.