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scDoc: correcting drop-out events in single-cell RNA-seq data.
Bioinformatics. 2020 May 04;:
Authors: Ran D, Zhang S, Lytal N, An L
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 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.
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: R code is available at https://github.com/anlingUA/scDoc.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID: 32365169 [PubMed – as supplied by publisher]
