scDoc: Correcting drop-out events in single-cell RNA-seq data

Di Ran, Shanshan Zhang, Nicholas Lytal, Lingling An

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of 'drop-out' events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. Results: scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. Availability and implementation: R code is available at https://github.com/anlingUA/scDoc.

Original languageEnglish (US)
Pages (from-to)4233-4239
Number of pages7
JournalBioinformatics
Volume36
Issue number15
DOIs
StatePublished - Aug 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'scDoc: Correcting drop-out events in single-cell RNA-seq data'. Together they form a unique fingerprint.

Cite this