Ecological patterns of the human microbiota exhibit high inter-subject variation, with few operational taxonomic units (OTUs) shared across individuals. To overcome these issues, non-parametric approaches, such as the Mann-Whitney U-test and Wilcoxon rank-sum test, have often been used to identify OTUs associated with host diseases. However, these approaches only use the ranks of observed relative abundances, leading to information loss, and are associated with high false-negative rates. In this study, we propose a phylogenetic tree-based microbiome association test (TMAT) to analyze the associations between microbiome OTU abundances and disease phenotypes. Phylogenetic trees illustrate patterns of similarity among different OTUs, and TMAT provides an efficient method for utilizing such information for association analyses. The proposed TMAT provides test statistics for each node, which are combined to identify mutations associated with host diseases.
Authors
Kangjin Kim, Ph.D Candidate <kangjin.kim1109@gmail.com>
Jaehyun Park, Ph.D Candidate <j31park@gmail.com>
Sang-Chul Park, Ph.D <cukucu123@gmail.com>
Sungho Won, Ph.D <won1@snu.ac.kr>
Written and maintained by Kangjin Kim.
| TMAT.R | R function for TMAT |
| User Reference Guide | A description for R function and example code |
| Example metagenome data file | An example metagenome data |
| Example metadata file | An example metadata |
| Silva 128 database tree file | The reference phylogenetic tree of Silva 128 database |
Kangjin Kim, Jaehyun Park, Sang-Chul Park, Sungho Won, (2020) Phylogenetic Tree-based Microbiome Association Test, Bioinformatics