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

Li Chen, James Reeve, Lujun Zhang, Shengbing Huang, Xuefeng Wang, Jun Chen

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere4600
JournalPeerJ
Volume2018
Issue number4
DOIs
StatePublished - 2018

Keywords

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

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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