Batch effects removal for microbiome data via conditional quantile regression

Wodan Ling, Jiuyao Lu, Ni Zhao (), Anju Lulla, Anna M. Plantinga, Weijia Fu, Angela Zhang, Hongjiao Liu, Hoseung Song, Zhigang Li, Jun Chen, Timothy W. Randolph, Wei Li A. Koay, James R. White, Lenore J. Launer, Anthony A. Fodor, Katie A. Meyer and Michael C. Wu ()
Additional contact information
Wodan Ling: Fred Hutchinson Cancer Center
Jiuyao Lu: Johns Hopkins Bloomberg School of Public Health
Ni Zhao: Johns Hopkins Bloomberg School of Public Health
Anju Lulla: University of North Carolina
Anna M. Plantinga: Williams College
Weijia Fu: University of Washington
Angela Zhang: Fred Hutchinson Cancer Center
Hongjiao Liu: Fred Hutchinson Cancer Center
Hoseung Song: Fred Hutchinson Cancer Center
Zhigang Li: University of Florida
Jun Chen: Mayo Clinic
Timothy W. Randolph: Fred Hutchinson Cancer Center
Wei Li A. Koay: Children’s National Hospital
James R. White: Resphera Biosciences
Lenore J. Launer: Laboratory of Epidemiology and Population Science, NIA
Anthony A. Fodor: University of North Carolina at Charlotte
Katie A. Meyer: University of North Carolina
Michael C. Wu: Fred Hutchinson Cancer Center

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.

Date: 2022
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DOI: 10.1038/s41467-022-33071-9

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