bioRxivBioinformaticsPreprint
GMPR: A novel normalization method for microbiome sequencing data
Abstract
SummaryNormalization is the first and a critical step in microbiome sequencing (microbiome-Seq) data analysis to account for variable library sizes. Though RNA-Seq based normalization methods have been adapted for microbiome-Seq data, they fail to consider the unique characteristics of microbiome-Seq data, which contain a vast number of zeros due to the physical absence or undersampling of the microbes. Normalization methods that specifically address the zeroinflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zeroinflated sequencing data such as microbiome-Seq data. Simulation studies and analyses of 38 real gut microbiome datasets from 16S rRNA gene amplicon sequencing demonstrated the superior performance of the proposed method.\n\nAvailability and Implementation GMPR is implemented in R andavailable at https://github.com/jchen1981/GMPR\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.\n\nContactChen.Jun2@mayo.edu
- Citations
- 2
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- TaxaNorm: a novel taxa-specific normalization approach for microbiome data
Ziyue Wang, Dillon Lloyd, … Alison A. Motsinger‐Reif·BMC BioinformaticsPeer-reviewed·Sep 16, 2024
- RSim: A Reference-Based Normalization Method via Rank Similarity
Bo Yuan, Shulei Wang·PLoS Computational BiologyPeer-reviewed·Sep 1, 2023
- Alpha and beta-diversities performance comparison between different normalization methods and centered log-ratio transformation in a microbiome public dataset
D. Bars-Cortina·bioRxiv·Nov 7, 2022
- A Realistic Simulation Framework for EvaluatingMicrobiome Normalization in Sample Stratification and Differential Abundance
Amen Khabeer Al Khafaji, Carolina Gómez‐Llorente, José Camacho·bioRxiv·Jan 21, 2026
- BMDD: A Probabilistic Framework for Accurate Imputation of Zero-inflated Microbiome Sequencing Data
Huijuan Zhou, Jun Chen, Xianyang Zhang·PLoS Computational BiologyPeer-reviewed·Oct 24, 2025
- Shrinkage improves estimation of microbial associations under different normalization methods
Michelle H. Badri, Zachary D. Kurtz, … Christian L. Müller·NAR Genomics and BioinformaticsPeer-reviewed·Dec 1, 2020