Batch effects removal for microbiome data via conditional quantile regression

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 ta...

Full description

Saved in:
Bibliographic Details
Published in:Nature communications Vol. 13; no. 1; pp. 5418 - 14
Main Authors: Ling, Wodan, Lu, Jiuyao, Zhao, Ni, Lulla, Anju, Plantinga, Anna M., Fu, Weijia, Zhang, Angela, Liu, Hongjiao, Song, Hoseung, Li, Zhigang, Chen, Jun, Randolph, Timothy W., Koay, Wei Li A., White, James R., Launer, Lenore J., Fodor, Anthony A., Meyer, Katie A., Wu, Michael C.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 15.09.2022
Nature Publishing Group
Nature Portfolio
Subjects:
49
49/47
631/114/1314
631/114/2415
631/114/794
631/326/2522
Gene expression
Health sciences
Humanities and Social Sciences
Metadata
Microbiomes
Microbiota - genetics
Microorganisms
Modelling
multidisciplinary
Public health
Regression models
Research Design
Science
Science (multidisciplinary)
Variables
ISSN:2041-1723, 2041-1723
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-33071-9