ReviewFrontiers in genetics2019
Multitable Methods for Microbiome Data Integration.
Review 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 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Evaluation of the Bacterial Diversity in the World's Deepest Cave-Veryovkina, Arabika Massif, Western Caucasus.Microorganisms · 2026Article
- A realistic simulation-based benchmark of microbiome normalization in sample stratification and taxa-level analysis.Frontiers in bioinformatics · 2026Article
- Review
- Pathway-Specific Insights into Colorectal Cancer Through Comprehensive Multi-Omics Data Integration.Biology · 2025Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- AI in microbiome-related healthcare.Microbial biotechnology · 2024Review
- Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.Ecology and evolution · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- Advances in multi-omics integrated analysis methods based on the gut microbiome and their applications.Frontiers in microbiology · 2024Review
- Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective.Journal of pharmaceutical analysis · 2023Review
- WormTensor: a clustering method for time-series whole-brain activity data from C. elegans.BMC bioinformatics · 2023Article
- Towards standardized and reproducible research in skin microbiomes.Environmental microbiology · 2022Review
- Statistical challenges in longitudinal microbiome data analysis.Briefings in bioinformatics · 2022Review
- Disentangling host-microbiota complexity through hologenomics.Nature reviews. Genetics · 2022Review
- Microbiome-based disease prediction with multimodal variational information bottlenecks.PLoS computational biology · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- Community-wide hackathons to identify central themes in single-cell multi-omics.Genome biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The simultaneous study of multiple measurement types is a frequently encountered problem in practical data analysis. It is especially common in microbiome research, where several sources of data-for example, 16s-rRNA, metagenomic, metabolomic, or transcriptomic data-can be collected on the same physical samples. There has been a proliferation of proposals for analyzing such multitable microbiome data, as is often the case when new data sources become more readily available, facilitating inquiry into new types of scientific questions. However, stepping back from the rush for new methods for multitable analysis in the microbiome literature, it is worthwhile to recognize the broader landscape of multitable methods, as they have been relevant in problem domains ranging across economics, robotics, genomics, chemometrics, and neuroscience. In different contexts, these techniques are called data integration, multi-omic, and multitask methods, for example. Of course, there is no unique optimal algorithm to use across domains-different instances of the multitable problem possess specific structure or variation that are worth incorporating in methodology. Our purpose here is not to develop new algorithms, but rather to 1) distill relevant themes across different analysis approaches and 2) provide concrete workflows for approaching analysis, as a function of ultimate analysis goals and data characteristics (heterogeneity, dimensionality, sparsity). Towards the second goal, we have made code for all analysis and figures available online at https://github.com/krisrs1128/multitable_review.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.