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, Michael C. Wu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number5418
JournalNature communications
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)


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