ArticlePLoS computational biology2019
Detecting interaction networks in the human microbiome with conditional Granger causality.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses 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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Next-generation probiotics: an outlook into current applications and future developments.Nature reviews. Microbiology · 2026Review
- From association to causation: a decision-aware framework for reproducible biomarker discovery and precision intervention design in the human gut microbiome.Briefings in bioinformatics · 2026Article
- Latitudinal Variation in Estuarine Archaeal Biogeography: Deterministic vs. Stochastic Assembly Processes and Network Stability Across China's Coastal Ecosystems.Microorganisms · 2026Article
- Improving DirectLiNGAM for high-dimensional microbiome data: roots screening and eBIC based model selection.Frontiers in systems biology · 2026Article
- On formal limitations of causal ecological networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- The MTIST platform: a microbiome time series inference standardized test.Research square · 2024Article
- Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review.Genes · 2023Review
- Intrahost evolution of the gut microbiota.Nature reviews. Microbiology · 2023Review
- Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions.Advances in nutrition (Bethesda, Md.) · 2022Article
- Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data.Nature communications · 2022Article
- Review
- Causality and correlation analysis for deciphering the microbial interactions in activated sludge.Frontiers in microbiology · 2022Article
- Network analyses in microbiome based on high-throughput multi-omics data.Briefings in bioinformatics · 2021Review
- Causal effects in microbiomes using interventional calculus.Scientific reports · 2021Article
- Positive interactions within and between populations decrease the likelihood of evolutionary rescue.PLoS computational biology · 2021Article
- Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities.Annual review of pharmacology and toxicology · 2021Review
- Emerging computational tools and models for studying gut microbiota composition and function.Current opinion in biotechnology · 2020Review
- Transformation of Microbial Negative Correlations into Positive Correlations by Saccharomyces cerevisiae Inoculation during Pomegranate Wine Fermentation.Applied and environmental microbiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because "correlation is neither necessary nor sufficient to establish causation"; environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiome to decipher the networks of correlation and causation of human-associated microbial genera. We show that correlation is not a good proxy for biological interaction; we observed a weak negative relationship between correlation and causality. Strong interspecific interactions are disproportionately positive, whereas almost all strong intraspecific interactions are negative. Interestingly, intraspecific interactions also appear to act at a short timescale causing vast majority of the effects within 1-3 days. We report how different taxa are involved in causal relationships with others, and show that strong interspecific interactions are rarely conserved across two body sites whereas strong intraspecific interactions are much more conserved, ranging from 33% between the gut and right-hand to 70% between the two hands. Therefore, in the absence of guiding assumptions about ecological interactions, Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.
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.