Abstract
The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.
Highlights
The scRNA-seq technologies quantify the heterogeneity of cell transcriptomes at a high resolution and discover novel cell types, which is superiority over bulk RNA-seq technologies [1,2,3,4,5]
The low amount of extracted mRNA leads to a large number of dropout events, which introduce computational challenges and hinder downstream analysis of data
We developed SDImpute, a novel statistical method to recover the scRNA-seq data based on cell-level and gene-level information in this manuscript
Summary
The scRNA-seq technologies quantify the heterogeneity of cell transcriptomes at a high resolution and discover novel cell types, which is superiority over bulk RNA-seq technologies [1,2,3,4,5]. Several methods were designed for dealing with the dropout events in the scRNA-seq data [15,16,17,18,19,20,21,22,23] These methods capture dropout features in different ways and implement imputation strategies by borrowing information from similar cells or similar genes. When the expression matrix is sparse, the expression levels of a gene in similar cells or the expression levels of similar genes in a cell are very likely to be affected by dropouts In this case, these methods relying on similar cells or similar genes are incapable of acquiring sufficient information to infer the accurate imputed values
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