ArticleFrontiers in genetics2019
A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- Work-Influenced Circadian Disruption Connected to Disease Risk but Not Microbiomes in a Cohort of Philadelphia Nurses.American journal of human biology : the official journal of the Human Biology Council · 2026Article
- Design, processing, and modeling for longitudinal multiomics microbiome data.Frontiers in cellular and infection microbiology · 2026Review
- Article
- LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer.Nature communications · 2025Article
- A multi-step completion process model of cell plasticity.Briefings in bioinformatics · 2025Article
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.eLife · 2024Review
- Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review.Genes · 2023Review
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Effects of Long-Term Storage on the Biobanked Neonatal Dried Blood Spot Metabolome.Journal of the American Society for Mass Spectrometry · 2023Article
- Bayesian multivariate sparse functional principal components analysis with application to longitudinal microbiome multiomics data.The annals of applied statistics · 2022Article
- Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data.Microorganisms · 2022Review
- SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.BMC bioinformatics · 2022Article
- Statistical challenges in longitudinal microbiome data analysis.Briefings in bioinformatics · 2022Review
- Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research.Toxicological sciences : an official journal of the Society of Toxicology · 2022Review
- Alterations in the Gut Fungal Community in a Mouse Model of Huntington's Disease.Microbiology spectrum · 2022Article
- Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture.Microbiome · 2022Article
- Interpretation of network-based integration from multi-omics longitudinal data.Nucleic acids research · 2022Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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