A deconvolution method for predicting cell abundance based on single cell RNAseq data It is important to understand the celltype 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 singlecell RNAsequencing (scRNAsq), using scRNAsq 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 RNASeq data to obtain the cell type composition in the tissue. Most of the existing methods require the signature matrix, which provides the cell typespecific 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 typespecific gene expression profile. To solve this problem, we propose a novel method, named DCap, to predict cell abundance. Different from nonnegative least squares, DCap performs weighted iterative calculation based on least squares. By weighting bulk tissue gene expression matrix and singlecell gene expression matrix, DCap minimizes the measurement error of Bulk RNASeq 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 nonnegative 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.

Singlecell RNAseq Imputation Based on Matrix Completion with Side Information Dropout events in singlecell RNA sequencing cause large numbers of zero values in gene expression matrices. Zero values hinder accurate downstream analysis of singlecell RNAseq (scRNAseq) data. In this study, to estimate the zero values in scRNAseq data, we proposed a novel method based on the low rank matrix completion approach. The novelty of the proposed method is due to using the gene association information as side information. We observed that incorporating the gene association information can facilitate more accurate recovery of gene expression matrices. To further improve the accuracy of zero imputation, we additionally employed a statistical model to estimate the dropout probability of each gene to adjust imputed gene expression matrices. We conducted extensive experiments to evaluate the performance of the proposed approach using several datasets. We compared the performance of the proposed method with that of three commonly used zero imputation methods in terms of accuracy in downstream clustering analysis. Results show that the proposed method has higher or comparable power for accurate zero imputation while has shorter run time compared to the commonly used zero imputation methods.