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• W3022607788 abstract "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. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W3022607788 created "2020-05-13" @default.
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• W3022607788 date "2020-05-04" @default.
• W3022607788 modified "2023-10-12" @default.
• W3022607788 title "scDoc: correcting drop-out events in single-cell RNA-seq data" @default.
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• W3022607788 doi "https://doi.org/10.1093/bioinformatics/btaa283" @default.
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