scTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation

The single-cell RNA sequencing (scRNA-seq) technique begins a new era by revealing gene expression patterns at single-cell resolution, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. However, existing large proportion of dropout events may hinder downstream analyses. Thus imputation of dropout events is an important step in analyzing scRNA-seq data. We develop scTSSR2, a new imputation method that combines matrix decomposition with the previously developed two-side sparse self-representation, leading to fast two-side sparse self-representation to impute dropout events in scRNA-seq data. The comparisons of computational speed and memory usage among different imputation methods show that scTSSR2 has distinct advantages in terms of computational speed and memory usage. Comprehensive downstream experiments show that scTSSR2 outperforms the state-of-the-art imputation methods. A user-friendly R package scTSSR2 is developed to denoise the scRNA-seq data to improve the data quality.

Automated summary

The single-cell RNA sequencing (scRNA-seq) technique is used to analyze gene expression patterns in complex tissues at single-cell resolution, but dropout events can hinder downstream analyses. We developed a new imputation method, scTSSR2, which combines matrix decomposition with two-side sparse self-representation to effectively impute dropout events in scRNA-seq data. Comparative experiments show that scTSSR2 outperforms existing imputation methods in terms of computational speed and memory usage. We also provide a user-friendly R package, scTSSR2, for denoising scRNA-seq data and improving data quality.