Mian: Interactive Web-Based 16S rRNA Operational Taxonomic Unit Table Data Visualization and Discovery Platform
Bibliographic record
Abstract
ABSTRACT In recent years, there has been strong interest in examining the microbiome and its impact on human health and the environment. By leveraging modern sequencing technologies, investigators can quickly determine the composition of a given microbial sample. At the same time, the same investigations often yield an array of categorical and numerical metadata derived from the sequenced samples such as immunohistochemical measures or locality information. Understanding how the microbiome data is associated with this external metadata is essential in developing targeted treatments for chronic diseases or proposing bacteria-modulated host responses. While many R or Python libraries and command-line tools have been developed for specific analysis purposes, there are still relatively few tools to facilitate open-ended data exploration and hypothesis generation. Here we introduce Mian, an open-source web framework to interactively visualize or run a suite of statistical and feature selection tools on the microbiome to identify important taxonomic groups in the context of any provided categorical or numerical metadata. Visualizations include boxplots, correlation networks, and PCA or NMDS scatterplots. Tools include Fisher’s Exact Test, Boruta feature selection, alpha and beta diversity, and differential and correlational analysis. Mian supports multiple standard representations of the OTU table as input and optionally subsamples the data during the upload process. Users can also filter and aggregate the OTU table at different taxonomic levels and dynamically adjust analysis parameters to see how the visualizations, results, and statistical measures change in real-time. Mian is freely available at: miandata.org
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.020 |
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".