Dataset Information

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

ABSTRACT: Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5696116 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

Zhang Yilong Y Han Sung Won SW Cox Laura M LM Li Huilin H

Genetic epidemiology 20170905 8

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput seque ...[more]

PMID: 28872698

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Project description:The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.

Dataset Information

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

Publications

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

Similar Datasets

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