amIcompositional: Simple Tests for Compositional Behaviour of High Throughput Data with Common Transformations
Bibliographic record
Abstract
Compositional approaches are beginning to permeate high throughput biomedical sciences in the areas of microbiome, genomics, transcriptomics and proteomics. Yet non-compositional approaches are still commonly observed. Non-compositional approaches are particularly problematic in network analysis based on correlation, ordination and exploratory data analysis based on distance, and differential abundance analysis based on normalization. Here we describe the aIc R package, a simple tool that answers the fundamental question: does the dataset or normalization exhibit compositional artefacts that will skew interpretations when analyzing high throughput biomedical data? The aIc R package includes options for several of the most widely used normalizations and filtering methods. The R package includes tests for subcompositional dominance and coherence along with perturbation and scale invariance. Exploratory analysis is facilitated by an R Shiny app that makes the process simple for those not wishing to use an R console. This simple approach will allow research groups to acknowledge and account for potential artefacts in data analysis resulting in more robust and reliable inferences.
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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 | 0.053 | 0.325 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".