Virtual Tissue Expression Analysis

Abstract

MotivationBulk RNA expression data is widely accessible, whereas single-cell data is relatively scarce in comparison. However, single-cell data offers profound insights into the cellular composition of tissues and cell-type-specific gene regulation, both of which remain hidden in bulk expression analysis. ResultsHere, we present tissueResolver an algorithm designed to extract single-cell type information from bulk data, enabling us to attribute expression changes to individual cell types. The outcome is a virtual tissue that can be analyzed in a manner similar to single-cell RNA-seq data. When validated on simulated data tissueResolver outperforms competing methods. Additionally, our study demonstrates that tissueResolver reveals previously overlooked celltype specific regulatory distinctions between the activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes of diffuse large B-cell lymphomas (DLBCL). Availability and ImplementationR package available at https://github.com/spang-lab/tissueResolver. Code for reproducing the results of this paper is available at https://github.com/spang-lab/tissueResolver-docs. Contactjakob.simeth@klinik.uni-regensburg.de