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Activity Number: 575 - Statistical Methods for Batch Effect Correction and Cell Type Deconvolution
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #304929 Presentation
Title: ComBat-Seq: Batch Correction Algorithm for RNA-Seq Count Data
Author(s): Yuqing Zhang* and Giovanni Parmigiani and W. Evan Johnson
Companies: Boston University and Dana-Farber Cancer Institute and Boston University
Keywords: Batch effect; Negative binomial regression; RNA-Seq; Data integration; Differential expression

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, unwanted variation in data caused by differences in technical factors across batches. It is therefore important to effectively address batch effects in genomic data. Many existing methods for batch adjustment assume continuous, bell-shaped Normal distribution for data. However in RNA-Seq studies where data are skewed, over-dispersed counts, this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used in differential expression methods to better describe the properties of counts. We developed a batch effect adjustment algorithm, ComBat-Seq, using a negative binomial regression. We show in realistic simulations and real data that ComBat-Seq adjusted data often yield higher statistical power in differential expression compared to other available methods, especially when batch effect is severe.

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

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