scTSSR2 : imputing dropout events for single-cell RNA sequencing using fast two-side self-representation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Bo Li
  • Ke Jin
  • Le Ou-Yang
  • Hong Yan
  • Xiao-Fei Zhang

Related Research Unit(s)


Original languageEnglish
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Publication statusOnline published - 27 Apr 2022


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 which 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.

Research Area(s)

  • Computational modeling, dropout, fast two-side self-representation, Gene expression, imputation, Matrix decomposition, matrix decomposition, Predictive models, RNA, ScRNA-seq, Sequential analysis, Sparse matrices