Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution.

ABSTRACT: Bulk RNA-seq deconvolution typically uses single-cell RNA-sequencing (scRNA-seq) references, but some cell types are only detectable through single-nucleus RNA sequencing (snRNA-seq). Because snRNA-seq captures nuclear, but not cytoplasmic, transcripts, direct use as a reference could reduce deconvolution accuracy. Here, we systematically benchmark strategies to integrate both modalities, focusing on transformations and gene-filtering approaches that harmonize snRNA-seq with scRNA-seq references. Across four diverse tissues, we evaluated principal component-based shifts, conditional and non-conditional variational autoencoders (scVI), and the removal of cross-modality differentially expressed genes (DEGs). While all methods improved performance relative to untransformed snRNA-seq, filterin

SUBMITTER: Ivich A 

PROVIDER: S-EPMC12393506 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature