DeconV: Probabilistic Cell Type Deconvolution from Bulk RNA-sequencing Data

Abstract

Bulk RNA-Seq remains a widely adopted technique to profile gene expression, primarily due to the persistent challenges associated with achieving single-cell resolution. However, a key challenge is accurately estimating the proportions of different cell types within these bulk samples. To address this issue, we introduce DeconV, a probabilistic framework for cell-type deconvolution that uses scRNA-Seq data as a reference. This approach aims to mitigate some of the limitations in existing methods by incorporating statistical frameworks developed for scRNA-Seq, thereby simplifying issues related to reference preprocessing such as normalization and marker gene selection. We benchmarked DeconV against established methods, including MuSiC, CIBERSORTx, and Scaden. Our results show that DeconV performs comparably in terms of accuracy to the best-performing method, Scaden, but provides additional interpretability by offering confidence intervals for its predictions. Furthermore, the modular design of DeconV allows for the investigation of discrepancies between bulk-sequenced samples and artificially generated pseudo-bulk samples.