Differential Abundance Analysis for Microbial Marker-Gene Surveys

Nat Methods. 2013 Dec;10(12):1200-2. doi: 10.1038/nmeth.2658. Epub 2013 Sep 29.

Abstract

We introduce a methodology to assess differential abundance in sparse high-throughput microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling-a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field.

MeSH terms

  • Algorithms
  • Animals
  • Area Under Curve
  • Cluster Analysis
  • Computer Simulation
  • Databases, Genetic
  • Gene Expression Profiling / methods
  • Genetic Markers*
  • Genetic Variation
  • Humans
  • Intestines / microbiology
  • Metagenomics / methods*
  • Mice
  • Microbiota*
  • Models, Genetic
  • Models, Statistical
  • Normal Distribution
  • Phenotype
  • RNA, Ribosomal, 16S / genetics*
  • Sequence Analysis, DNA
  • Software

Substances

  • Genetic Markers
  • RNA, Ribosomal, 16S