scTSSR : gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

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

9 Scopus Citations
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Author(s)

  • Ke Jin
  • Le Ou-Yang
  • Xing-Ming Zhao
  • Hong Yan
  • Xiao-Fei Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3131-3138
Journal / PublicationBioinformatics
Volume36
Issue number10
Online published19 Feb 2020
Publication statusPublished - 15 May 2020

Abstract

Motivation:  Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. 
Results:  In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. 
Availability and implementation:  The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. 
Contact:  zhangxf@mail.ccnu.edu.cn 
Supplementary information: Supplementary data are available at Bioinformatics online.

Citation Format(s)

scTSSR : gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation. / Jin, Ke; Ou-Yang, Le; Zhao, Xing-Ming; Yan, Hong; Zhang, Xiao-Fei.

In: Bioinformatics, Vol. 36, No. 10, 15.05.2020, p. 3131-3138.

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