ComBat-Seq: Batch Effect Adjustment for RNA-Seq Count Data

• Activity Number: 121 - In the Pipeline: Statistical Advances to Preserve Biological Signal in High-Throughput, Single-Cell Imaging and Sequencing Methods
• Type: Topic-Contributed
• Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): W. Evan Johnson* and Yuqing Zhang and Giovanni Parmigiani
• Companies: Boston University School of Medicine and Gilead Sciences, Inc. and Harvard University
• Keywords: ComBat; Batch correction; RNA-seq

Abstract

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches.It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in simulations and real data that ComBat-seq results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods.