False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing
Author(s) -
Jian Xiao,
Hongyuan Cao,
Jun Chen
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/btx311
Subject(s) - phylogenetic tree , microbiome , false discovery rate , tree (set theory) , computational biology , biology , metagenomics , dna sequencing , statistical power , data mining , computer science , evolutionary biology , bioinformatics , genetics , statistics , gene , mathematics , mathematical analysis
Next generation sequencing technologies have enabled the study of the human microbiome through direct sequencing of microbial DNA, resulting in an enormous amount of microbiome sequencing data. One unique characteristic of microbiome data is the phylogenetic tree that relates all the bacterial species. Closely related bacterial species have a tendency to exhibit a similar relationship with the environment or disease. Thus, incorporating the phylogenetic tree information can potentially improve the detection power for microbiome-wide association studies, where hundreds or thousands of tests are conducted simultaneously to identify bacterial species associated with a phenotype of interest. Despite much progress in multiple testing procedures such as false discovery rate (FDR) control, methods that take into account the phylogenetic tree are largely limited.
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