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PeerJ. 2018 Apr 2;6:e4600. doi: 10.7717/peerj.4600. eCollection 2018.

GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data.

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Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.
Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, MN, USA.
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, China.
Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.


Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero-inflation remain largely undeveloped. Here we propose geometric mean of pairwise ratios-a simple but effective normalization method-for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.


Metagenomics; Microbiome; Normalization; RNA-seq; Statistics; Zero-inflation

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