Differential abundance analysis for microbial marker-gene surveys
TLDR
It is shown that metagenomeSeq outperforms the tools currently used in this field and relies on a novel normalization technique and a statistical model that accounts for undersampling in large-scale marker-gene studies.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.read more
Citations
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QIIME allows analysis of high-throughput community sequencing data.
J. Gregory Caporaso,Justin Kuczynski,Jesse Stombaugh,Kyle Bittinger,Frederic D. Bushman,Elizabeth K. Costello,Noah Fierer,Antonio Gonzalez Peña,Julia K. Goodrich,Jeffrey I. Gordon,Gavin A. Huttley,Scott T. Kelley,Dan Knights,Jeremy E. Koenig,Ruth E. Ley,Catherine A. Lozupone,Daniel McDonald,Brian D. Muegge,Meg Pirrung,Jens Reeder,Joel Sevinsky,Peter J. Turnbaugh,William A. Walters,Jeremy Widmann,Tanya Yatsunenko,Jesse R. Zaneveld,Rob Knight,Rob Knight +27 more
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Metagenomic biomarker discovery and explanation
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TL;DR: A new method for metagenomic biomarker discovery is described and validates by way of class comparison, tests of biological consistency and effect size estimation to address the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities.
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