CDSeq A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data

摘要

Understanding the cellular composition of bulk tissues is critical to investigate the underlying mechanisms of many biological processes. Single cell sequencing is a promising technique, however, it is expensive and the analysis of single cell data is non-trivial. Therefore, tissue samples are still routinely processed in bulk. To estimate cell-type composition using bulk gene expression data, computational deconvolution methods are needed. Many deconvolution methods have been proposed, however, they often estimate only cell type proportions using a reference cell type gene expression profile, which in many cases may not be available. We present a novel complete deconvolution method that uses only bulk gene expression data to simultaneously estimate cell-type-specific gene expression profiles and sample-specific cell-type proportions. We showed that, using multiple RNA-Seq and microarray datasets where the cell-type composition was previously known, our method could accurately determine the cell-type composition. By providing a method that requires a single input to determine both cell-type proportion and cell-type-specific expression profiles, we expect that our method will be beneficial to biologists and facilitate the research and identification of mechanisms underlying many biological processes.