Semi-reference based cell type deconvolution with application to human metastatic cancers

Abstract

Abstract Bulk RNA-seq experiments, commonly used to discern gene expression changes across conditions, often neglect critical cell type-specific information due to their focus on average transcript abundance. Recognizing cell type contribution is crucial to understanding phenotype and disease variations. The advent of single-cell RNA sequencing has allowed detailed examination of cellular heterogeneity; however, the cost and analytic caveat prohibits such sequencing for a large number of samples. We introduce a novel deconvolution approach, SECRET, that employs cell type-specific gene expression profiles from single-cell RNA-seq to accurately estimate cell type proportions from bulk RNA-seq data. Notably, SECRET can adapt to scenarios where the cell type present in the bulk data is unrepresented in the reference, thereby offering increased flexibility in reference selection. SECRET has demonstrated superior accuracy compared to existing methods using synthetic data and has identified unknown tissue-specific cell types in real human metastatic cancers. Its versatility makes it broadly applicable across various human cancer studies.