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Activity Number: 351 - Statistical Methods for Single-Cell Genomics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #305173
Title: Flexible Experimental Designs for Valid Single-Cell RNA-Sequencing Experiments Allowing Batch Effects Correction
Author(s): Fangda Song* and Yingying Wei
Companies: The Chinese University of Hong Kong and The Chinese University of Hong Kong
Keywords: Batch effects; Experimental design; Single-cell RNA-seq experiments; Model-based clustering; Integrative analysis

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. Here, we mathematically prove that under two more flexible and realistic experimental designs--the "reference panel" and the "chain-type" 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.

Authors who are presenting talks have a * after their name.

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