Tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies

Tianhua Liao,Mingjing Luo,Yuchen Wei,Guo-Ping Zhao,Haokui Zhou

doi:10.1186/s13059-019-1871-4

Abstract

Untangling the complex variations of microbiome associated with large-scale host phenotypes or environment types challenges the currently available analytic methods. Here, we present tmap, an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies. The performance of tmap in detecting nonlinear patterns is validated by different scenarios of simulation, which clearly demonstrate its superiority over the most commonly used methods. Application of tmap to several population-scale microbiomes extensively demonstrates its strength in revealing microbiome-associated host or environmental features and in understanding the systematic interrelations among their association patterns. tmap is available at https://github.com/GPZ-Bioinfo/tmap.

Highlights

Microbiome-wide association studies (MWAS) capture the variation and dynamics of microbiome associated with host phenotypes or environment types [1,2,3,4,5]
Our method successfully identified most of the simulated nonlinear associations, which are hard to be detected with other methods
We found that some of the Environment Ontology (ENVO) descriptors had spatial analysis of functional enrichment (SAFE) enriched scores comparable to that of EMP Ontology (EMPO) classes (Fig. 7b)

Summary

Introduction

Microbiome-wide association studies (MWAS) capture the variation and dynamics of microbiome associated with host phenotypes or environment types [1,2,3,4,5]. We performed ordination analysis of the SAFE scores to reveal the interrelations between the host covariates and taxa accounting for the variation of the AGP microbiomes (Additional file 8: Figure S8, see the “Methods” section).

Results

Conclusion