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GMPR: A novel normalization method for microbiome sequencing data

Li Chen, Jun Chen
doi: https://doi.org/10.1101/112565
Li Chen
1Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA
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Jun Chen
2Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA
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ABSTRACT

Summary Normalization 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.

Availability and Implementation ‘GMPR’ is implemented in R andavailable at https://github.com/jchen1981/GMPR

Supplementary Information Supplementary data are available at Bioinformatics online.

Contact Chen.Jun2{at}mayo.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 28, 2017.
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GMPR: A novel normalization method for microbiome sequencing data
Li Chen, Jun Chen
bioRxiv 112565; doi: https://doi.org/10.1101/112565
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GMPR: A novel normalization method for microbiome sequencing data
Li Chen, Jun Chen
bioRxiv 112565; doi: https://doi.org/10.1101/112565

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