Estimating cell type composition using isoform expression one gene at a time

Human tissue samples are often mixtures of heterogeneous cell types, which can confound the analyses of gene expression data derived from such tissues. The cell type composition of a tissue sample may itself be of interest and is needed for proper analysis of differential gene expression. A variety of computational methods have been developed to estimate cell type proportions using gene‐level expression data. However, RNA isoforms can also be differentially expressed across cell types, and isoform‐level expression could be equally or more informative for determining cell type origin than gene‐level expression. We propose a new computational method, IsoDeconvMM, which estimates cell type fractions using isoform‐level gene expression data. A novel and useful feature of IsoDeconvMM is that it can estimate cell type proportions using only a single gene, though in practice we recommend aggregating estimates of a few dozen genes to obtain more accurate results. We demonstrate the performance of IsoDeconvMM using a unique data set with cell type–specific RNA‐seq data across more than 135 individuals. This data set allows us to evaluate different methods given the biological variation of cell type–specific gene expression data across individuals. We further complement this analysis with additional simulations.