scTSSR-D: Gene Expression Recovery by Two-side Self-Representation and Dropout Information for scRNA-seq Data

Abstract

Background: Single-cell RNA sequencing is an advanced technology that makes it possible to unravel cellular heterogeneity and conduct single-cell analysis of gene expression. However, owing to technical defects, many dropout events occur during sequencing, bringing about adverse effects on downstream analysis. Methods: To solve the dropout events existing in single-cell RNA sequencing, we propose an imputation method scTSSR-D, which recovers gene expression by two-side self-representation and dropout information. scTSSR-D is the first global method that combines a partial imputation method to impute dropout values. In other words, we make full use of genes, cells, and dropout information when recovering the gene expression. Results: The results show scTSSR-D outperforms other existing methods in the following experiments: capturing the Gini coefficient and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization, down-sampling experiments, differential expression analysis, and the accuracy of cell clustering. Conclusion: scTSSR-D is a more stable and reliable method to recover gene expression. Meanwhile, our method improves even more dramatically on large datasets compared to the result of existing methods.