Robust Phylogenetic Tree-based Microbiome Association Test using Repeatedly Measured Data for Composition Bias
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Abstract
Motivation
The effects of microbiota on the host phenotypes can substantially differ depending on his/her age. Longitudinally measured microbiome data allows us to detect the age modification effect and are useful for the detection of microorganisms related to the progression of disease which change identification over time. Moreover, longitudinal analysis enables the estimation of within-subject covariate effect, is robust against the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, they suffer from compositional bias, and few statistical methods can estimate their effect on host diseases with repeatedly measured 16S rRNA gene data. In this article, we proposed mTMAT which can be applied to longitudinal microbiome data and is robust against compositional bias.
Results
mTMAT normalized the microbial abundance and utilized the ratio of the pooled abundances for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. With extensive simulation studies, we showed that mTMAT is statistically more powerful and is robust against compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insight into bacterial pathology.
Availability
The 16S rRNA amplicon sequencing metagenomics datasets for Korea Association REsource cohort is available from the NCBI Sequence Read Archive database under project accession number PRJNA716550. mTMAT was implemented in the R package. Detailed information is available at https://healthstat.snu.ac.kr/software/mtmat .
Contact
won1@snu.ac.kr
Supplementary information
Supplementary data are available at Bioinformatics online.
Article activity feed
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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.
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