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Bodein A, Chapleur O, Droit A, et al. A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types. Front Genet. 2019;10:963doi: 10.3389/fgene.2019.00963.
Bodein, A., Chapleur, O., Droit, A., & Lê Cao, K. A. (2019). A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types. Frontiers in genetics, 10963. https://doi.org/10.3389/fgene.2019.00963
Bodein, Antoine, et al. "A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types." Frontiers in genetics vol. 10 (2019): 963. doi: https://doi.org/10.3389/fgene.2019.00963
Bodein A, Chapleur O, Droit A, Lê Cao KA. A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types. Front Genet. 2019 Nov 07;10:963. doi: 10.3389/fgene.2019.00963. eCollection 2019. PMID: 31803221; PMCID: PMC6875829.
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Front Genet. 2019 Nov 07;10:963. doi: 10.3389/fgene.2019.00963. eCollection 2019.

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types.

Frontiers in genetics

Antoine Bodein, Olivier Chapleur, Arnaud Droit, Kim-Anh Lê Cao

Affiliations

  1. Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada.
  2. Hydrosystems and Biopresses Research Unit, Irstea, Antony, France.
  3. Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia.

PMID: 31803221 PMCID: PMC6875829 DOI: 10.3389/fgene.2019.00963

Abstract

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

Copyright © 2019 Bodein, Chapleur, Droit and Lê Cao.

Keywords: data integration; dimension reduction; feature selection; multi-omics; splines; time course

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