Factorgrams: A tool for visualizing multi-way associations in biological data (University of Toronto
Bibliographic record
Abstract
Effective visualization of biological data is often critical for subsequent analysis. The popular clustergram/dendrogram visualization rearranges rows and columns of a data matrix so as to highlight clusters of similar responses, but assumes each row or column belongs to only one cluster and cannot associate each row or column with multiple clusters. Such multi-way associations occur frequently, e.g., when a gene plays multiple biological roles. We describe the ’factorgram ’ visualization, which rearranges the data into an expanded view, associating each row (or column) with multiple clusters of rows (or columns) and elucidating potentially new biological relationships. Factorgrams for mouse gene expression and yeast synthetic-lethal gene-interaction datasets detect a larger number of statistically-significant clusters than clustergrams, plus a larger number of clusters enriched for gene ontology annotations. Experimentally-verified associations previously identified by manual rearrangement of rows and columns not grouped together by clustergrams, are readily identified by the factorgram. Factorgrams: A tool for visualizing multi-way associations in biological data
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.022 |
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".