A novel method for predicting cell abundance based on single-cell RNA-seq data

• Published in: BMC bioinformatics Vol. 22; no. S9; pp. 1 - 15
• Main Authors: Peng, Jiajie, Han, Lu, Shang, Xuequn
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 25.08.2021; BioMed Central; BMC
• Subjects: Abundance; Analysis; Bioinformatics; Cell abundance prediction; Composition; Datasets; Deconvolution; Gene expression; Least squares; Mathematical analysis; Medical treatment; Methods; Music; Noise measurement; Predictions; Ribonucleic acid; RNA; RNA sequencing; Simulation; Statistical analysis; Tissues; Weighted least squares; China
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04187-4

It is important to understand the composition of cell type and its proportion in intact tissues, as changes in certain cell types are the underlying cause of disease in humans. Although compositions of cell type and ratios can be obtained by single-cell sequencing, single-cell sequencing is currently expensive and cannot be applied in clinical studies involving a large number of subjects. Therefore, it is useful to apply the bulk RNA-Seq dataset and the single-cell RNA dataset to deconvolute and obtain the cell type composition in the tissue. By analyzing the existing cell population prediction methods, we found that most of the existing methods need the cell-type-specific gene expression profile as the input of the signature matrix. However, in real applications, it is not always possible to find an available signature matrix. To solve this problem, we proposed a novel method, named DCap, to predict cell abundance. DCap is a deconvolution method based on non-negative least squares. DCap considers the weight resulting from measurement noise of bulk RNA-seq and calculation error of single-cell RNA-seq data, during the calculation process of non-negative least squares and performs the weighted iterative calculation based on least squares. By weighting the bulk tissue gene expression matrix and single-cell gene expression matrix, DCap minimizes the measurement error of bulk RNA-Seq and also reduces errors resulting from differences in the number of expressed genes in the same type of cells in different samples. Evaluation test shows that DCap performs better in cell type abundance prediction than existing methods. DCap solves the deconvolution problem using weighted non-negative least squares to predict cell type abundance in tissues. DCap has better prediction results and does not need to prepare a signature matrix that gives the cell-type-specific gene expression profile in advance. By using DCap, we can better study the changes in cell proportion in diseased tissues and provide more information on the follow-up treatment of diseases.