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 journal › peer-review
Related Research Unit(s)
|Journal / Publication||Bioinformatics|
|Online published||19 Feb 2020|
|Publication status||Published - 15 May 2020|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85084696218&origin=recordpage|
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.
Supplementary information: Supplementary data are available at Bioinformatics online.
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.