Testing hypotheses about the microbiome using the linear decomposition model (LDM)
Author(s) -
YiJuan Hu,
Glen A. Satten
Publication year - 2020
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/btaa260
Subject(s) - microbiome , false discovery rate , type i and type ii errors , computer science , statistical power , statistical hypothesis testing , ordination , covariate , multiple comparisons problem , permutation (music) , transformation (genetics) , data mining , statistics , machine learning , biology , mathematics , bioinformatics , biochemistry , physics , acoustics , gene
Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR).
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