A systematic benchmark of integrative strategies for microbiome-metabolome data
Bibliographic record
Abstract
The rapid advancement of high-throughput sequencing technologies has enabled the integration of various omic layers into computational frameworks. Among these, metagenomics and metabolomics are increasingly studied for their roles in complex diseases. However, no standard currently exists for jointly integrating microbiome and metabolome datasets within statistical models. We benchmarked nineteen integrative methods to disentangle the relationships between microorganisms and metabolites. These methods address key research goals, including global associations, data summarization, individual associations, and feature selection. Through realistic simulations, we identified the best-performing methods and validated them on real gut microbiome datasets, revealing complementary biological processes across the two omic layers. Practical guidelines are provided for specific scientific questions and data types. This work establishes a foundation for research standards in metagenomics-metabolomics integration and supports future methodological developments, while also providing guidance for designing optimal analytical strategies tailored to specific integration questions. Using realistic simulations, statistical methods to integrate microbiome and metabolome data are assessed and those best suited for different tasks are identified. Recommendations for method selection depending on the aim and data structure are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".