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SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data

By Yiliang Zhang, Kexuan Liang, Molei Liu, Yue Li, Hao Ge, Hongyu Zhao

Posted 04 Oct 2019
bioRxiv DOI: 10.1101/793463

Single-cell RNA sequencing technologies are widely used in recent years as a powerful tool allowing the observation of gene expression at the resolution of single cells. Two of the major challenges in scRNA-seq data analysis are dropout events and batch effects. The inflation of zero(dropout rate) varies substantially across single cells. Evidence has shown that technical noise, including batch effects, explains a notable proportion of this cell-to-cell variation. To capture biological variation, it is necessary to quantify and remove technical variation. Here, we introduce SCRIBE (Single-Cell Recovery Imputation with Batch Effects), a principled framework that imputes dropout events and corrects batch effects simultaneously. We demonstrate, through real examples, that SCRIBE outperforms existing scRNA-seq data analysis tools in recovering cell-specific gene expression patterns, removing batch effects and retaining biological variation across cells. Our software is freely available online at <https://github.com/YiliangTracyZhang/SCRIBE>.

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