Nature Communications (Sep 2022)
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
Affiliations
- Wodan Ling
- Public Health Sciences Division, Fred Hutchinson Cancer Center
- Jiuyao Lu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
- Ni Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
- Anju Lulla
- Nutrition Research Institute and Department of Nutrition, University of North Carolina
- Anna M. Plantinga
- Department of Mathematics and Statistics, Williams College
- Weijia Fu
- Department of Biostatistics, School of Public Health, University of Washington
- Angela Zhang
- Public Health Sciences Division, Fred Hutchinson Cancer Center
- Hongjiao Liu
- Public Health Sciences Division, Fred Hutchinson Cancer Center
- Hoseung Song
- Public Health Sciences Division, Fred Hutchinson Cancer Center
- Zhigang Li
- Department of Biostatistics, College of Public Health & Health Professions, College of Medicine, University of Florida
- Jun Chen
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic
- Timothy W. Randolph
- Public Health Sciences Division, 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
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte
- Katie A. Meyer
- Nutrition Research Institute and Department of Nutrition, University of North Carolina
- Michael C. Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Center
- DOI
- https://doi.org/10.1038/s41467-022-33071-9
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 14
Abstract
Here, the authors present ConQuR, a conditional quantile regression method that removes microbiome batch effects through non-parametric modeling of complex microbial read counts, while preserving the signals of interest.
