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An omnibus test for differential distribution analysis of microbiome sequencing data
Author(s) -
Jun Chen,
Emily A. King,
Rebecca A. Deek,
Zhi Wei,
Yue Yu,
Diane E. Grill,
Karla V. Ballman
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx650
Subject(s) - outlier , microbiome , covariate , computer science , count data , negative binomial distribution , metagenomics , data mining , parametric statistics , statistics , biology , bioinformatics , machine learning , mathematics , artificial intelligence , poisson distribution , genetics , gene
One objective of human microbiome studies is to identify differentially abundant microbes across biological conditions. Previous statistical methods focus on detecting the shift in the abundance and/or prevalence of the microbes and treat the dispersion (spread of the data) as a nuisance. These methods also assume that the dispersion is the same across conditions, an assumption which may not hold in presence of sample heterogeneity. Moreover, the widespread outliers in the microbiome sequencing data make existing parametric models not overly robust. Therefore, a robust and powerful method that allows covariate-dependent dispersion and addresses outliers is still needed for differential abundance analysis.

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