A Deconvolution Method For Predicting Cell Abundance Based On Single Cell Rna-Seq Data

It is important to understand the cell-type composition and its proportion in tissues. Some previous experiments have shown that the variation of gene expression in certain cell types may lead to disease. Although cell type composition and proportion can be obtained by single-cell RNA-sequencing (scRNA-sq), using scRNA-sq is expensive and cannot be applied in clinical studies involving a large number of subjects currently. Therefore, it is urgent to develop a method to deconvolute the Bulk RNA-Seq data to obtain the cell type composition in the tissue. Most of the existing methods require the signature matrix, which provides the cell type-specific gene expression profile, as input. However, the signature matrix is not always available for some types of tissue, and it is not always possible to find a suitable cell type-specific gene expression profile. To solve this problem, we propose a novel method, named DCap, to predict cell abundance. Different from non-negative least squares, DCap performs weighted iterative calculation based on least squares. By weighting bulk tissue gene expression matrix and single-cell gene expression matrix, DCap minimizes the measurement error of Bulk RNA-Seq and error resulting from the difference in the amount of genes in the same cell type among different samples. DCap solves the deconvolution problem by using weighted non-negative least squares to predict cell type abundance. DCap does not need to prepare a suitable signature matrix in advance, and the evaluation test shows that DCap performs better in cell type abundance prediction than existing methods.