Title: Batch effects correction for single-cell RNA sequencing data
Authors: Fangda Song - The Chinese University of Hong Kong (Hong Kong) [presenting]
Yingying Wei - The Chinese University of Hong Kong (Hong Kong)
Abstract: Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. We mathematically prove that under two designs---true biological variability can also be separated from batch effects. We develop Batch Effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.