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

Single-cell RNA sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell 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 paper, 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. scDoc is the first method that involves drop-out information to account 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 can impute the drop-out events more accurately and robustly; specifically, it outperforms all available imputation methods in reference to data visualization, cell subpopulation identification, and differential expression detection in scRNA-seq data.

Original languageEnglish (US)
JournalUnknown Journal
DOIs
StatePublished - Aug 10 2019

Keywords

  • cell-to-cell similarity
  • drop-outs
  • imputation
  • RNA-seq
  • single cell

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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