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

EasyChair Preprint no. 1591

Abstract

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.

Keyphrases: batch effect, batch effect correction, Batch effects correction, Imputation, mixed model, Monte-Carlo EM algorithm, mouse neuron dataset, silhouette width, Single-cell RNA-sequencing data, uniform manifold approximation

@Booklet{EasyChair:1591,
  author = {Yiliang Zhang and Kexuan Liang and Molei Liu and Yue Li and Hao Ge and Hongyu Zhao},
  title = {SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data},
  howpublished = {EasyChair Preprint no. 1591},

  year = {EasyChair, 2019}}