Open Accesspldist: ecological dissimilarities for paired and longitudinal microbiome association analysis
Author(s) -
Anna Plantinga,
Jun Chen,
Robert R. Jenq,
Michael C. Wu
Publication year - 2019
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/btz120
Subject(s) - unifrac , microbiome , statistical power , phylogenetic tree , range (aeronautics) , type i and type ii errors , computer science , r package , ordination , confounding , transformation (genetics) , computational biology , biology , data mining , statistics , machine learning , bioinformatics , mathematics , genetics , materials science , 16s ribosomal rna , computational science , bacteria , gene , composite material
The human microbiome is notoriously variable across individuals, with a wide range of 'healthy' microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce.
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