Systematic assessment of the biological impact of cellular deconvolution on downstream analyses of disease transcriptomes

Abstract

Background Cellular deconvolution methods estimate cell type proportions from bulk RNA seq data, typically using single cell RNA seq derived signatures, enabling separation of disease associated transcriptional changes into composition driven and cell intrinsic effects. However, these approaches depend on model assumptions and the stability of cell type signatures, and it remains unclear how deconvolution related uncertainties influence downstream analyses and biological conclusions. Results We systematically evaluated the effect of cell type correction on disease relevant transcriptomic insights, using Alzheimer's disease (AD) as a model and the Mount Sinai Brain Bank cohort as a primary dataset. Applying dtangle, selected after comparison with another deconvolution approach, we estimated cell type proportions across four brain regions and assessed how correction reshaped differential gene expression and pathway enrichment. Cell type correction (CTC) markedly altered differentially expressed gene (DEG) profiles in a region dependent manner: the superior temporal gyrus lost all significant signals, while the frontal pole gained DEGs with improved cross region concordance. At the pathway level, correction shifted enrichment from synaptic loss and immune activation toward suppression of stress response and immune regulatory programs, suggesting that composition changes partly obscure cell intrinsic regulatory signals. Overlap with AD genome wide association study loci and replication in an independent cohort indicated that cell intrinsic changes are more consistently validated than composition driven changes. Notably, KCNN2 and RIMS1, not currently recognized as canonical AD biomarkers, emerged as robust transcriptional signatures, potentially reflecting both compositiondriven and cell intrinsic dysregulation and warranting further investigation. Conclusions Parallel evaluation of uncorrected and CTC analyses distinguishes composition driven from cell intrinsic transcriptional effects and highlights robust disease signatures in heterogeneous tissues such as the brain.