Robust phylogenetic tree-based microbiome association test using repeatedly measured data for composition bias
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (Arcadia Science)
Abstract
Background
The effects of microbiota on the host phenotypes can differ substantially depending on their age. Longitudinally measured microbiome data allow for the detection of the age modification effect and are useful for the detection of microorganisms related to the progression of disease whose identification change over time. Moreover, longitudinal analysis facilitates the estimation of the within-subject covariate effect, is robust to the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, this method of analysis is limited by compositional bias, and few statistical methods can estimate the effect of microbiota on host diseases with repeatedly measured 16S rRNA gene data. Herein, we propose mTMAT, which is applicable to longitudinal microbiome data and is robust to compositional bias.
Results
mTMAT normalized the microbial abundance and utilized the ratio of the pooled abundance for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. The robustness of mTMAT against compositional bias is underscored by its utilization of abundance ratios.
Conclusions
With extensive simulation studies, we showed that mTMAT is statistically relatively powerful and is robust to compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insights into bacterial pathology.
Article activity feed
-
-
I'm very excited by mTMAT. In the past, I've struggled to find robust statistical techniques to analyze longitudinal microbiome data, so I'm very excited to have another method in my toolkit. After reading your preprint, I have a few questions about the versatility of mTMAT.
- Would it be possible to use mTMAT on shotgun metagenomic data? I'm envisioning longitudinal samples for which species or strain counts have been inferred.
- How would mTMAT behave in the face of large microbial transitions over time. For example, what about microbiomes where the composition fluctuates diurnally? Or, a large change happens over the course of a year (like the onset of inflammatory bowel disease)
- The language at the beginning of the paper emphasis association with disease state. I'm curious if mTMAT is flexible for application to any variable set. For …
I'm very excited by mTMAT. In the past, I've struggled to find robust statistical techniques to analyze longitudinal microbiome data, so I'm very excited to have another method in my toolkit. After reading your preprint, I have a few questions about the versatility of mTMAT.
- Would it be possible to use mTMAT on shotgun metagenomic data? I'm envisioning longitudinal samples for which species or strain counts have been inferred.
- How would mTMAT behave in the face of large microbial transitions over time. For example, what about microbiomes where the composition fluctuates diurnally? Or, a large change happens over the course of a year (like the onset of inflammatory bowel disease)
- The language at the beginning of the paper emphasis association with disease state. I'm curious if mTMAT is flexible for application to any variable set. For example, could I apply this tool to wine microbiomes? I assume yes from the way the pregnancy data set is presented but wanted to check.
-
