Conference Agenda

The high cost of single-cell sequencing often compels large cohort studies to rely on bulk RNA-seq, which presents challenges in resolving tissue heterogeneity and understanding the roles of individual cell types. In bulk RNA-seq analysis, deconvolution is essential for extracting cell-type-specific information. Most tools focus on estimating cell type proportions, but only a few aim to infer cell-type-specific gene expression profiles (ctsGEPs). Among these, very few estimate ctsGEPs at the individual sample level. The technical challenges of this task highlight the need for more advanced approaches capable of generating accurate individual-level ctsGEP estimates. Such estimates are critical for downstream analyses, including cell-type-specific differential expression and expression quantitative trait locus studies.

To address this, we developed a novel deconvolution method to estimate individual-level ctsGEPs and cell type proportions simultaneously from bulk RNA-seq data. Using a hierarchical Bayesian framework, our method captures the stochastic variation of ctsGEPs across individuals. Parameters are estimated via Markov Chain Monte Carlo (MCMC), with hyperparameters optimized for robust inference.

We benchmarked our method using 48 in silico mixtures generated from single-cell RNA-seq data of human brain donors. The results demonstrated strong performance, with correlations of ~0.9 for ctsGEP estimates and >0.6 for gene expression variation across samples for ~80% of genes. Our method outperformed existing tools, reducing Root-Mean-Square Errors by ~16%. Additionally, we showcased its application in cell-type-specific differential expression analysis.

Our method provides a powerful tool to computationally unravel cell-type-specific expression profiles in bulk RNA-seq data, enabling advances in understanding cellular heterogeneity in biological and pathological contexts.