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

BackgroundCellular 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. ResultsWe systematically evaluated the effect of cell-type correction on disease-relevant transcriptomic insights, using Alzheimers 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 composition-driven and cell-intrinsic dysregulation and warranting further investigation. ConclusionsParallel 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.