Journal
BIOINFORMATICS
Volume 36, Issue 15, Pages 4233-4239Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa283
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Funding
- United States Department of Agriculture [ARZT-1360830-H22-138, ARZT-1361620-H22-149]
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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.
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