Beta-diversity distance matrices for microbiome sample size and power calculations — How to obtain good estimates
Bibliographic record
Abstract
In microbiome studies, researchers often wish to compare the taxa count distributions between groups of samples. Commonly-used corresponding methods of analysis are built on examining distance matrices, where distances describe the beta-diversity between samples. Analyses then compare the distribution of distances within groups to the distributions between groups. However, when performing a priori sample size or power calculations for such study designs, appropriate within and between group distance distributions can be challenging to obtain. When available, pilot study data, or data from prior studies of similar design should provide realistic distance estimates. However, when these are not available, distances can be extracted from available studies where one can assume similar beta-diversity. Alternatively, distances can be generated by simulation methods. Here, we describe and illustrate these three strategies for obtaining realistic distance matrices. For simulation methods, we illustrate the procedures required starting from existing benchmark data, as well as how to simulate directly from population assumptions. Using data from the American Gut project, we provide tables of observed distances for use by researchers planning their own studies, as well as R codes for generating similar matrices in other datasets. Furthermore, for simulated data, we compare methods, provide R codes, and demonstrate how challenging it is to obtain realistic distance distributions without any benchmark data. This code and illustrative distance tables are provided by the IMPACTT Consortium as a resource to the microbiome research community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.440 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".