Gene batteries and synexpression groups applied in a multivariate statistical approach to dose–response analysis of toxicogenomic data
Bibliographic record
Abstract
Univariate statistical analyses have limited strength when employed in low-dose toxicogenomic studies, due to diminished magnitudes and frequencies of gene expression responses, compounded by high data dimensionality. Analysis using co-regulated gene sets and a multivariate statistical test based upon ranks of expression were explored as means to improve statistical confidence and biological insight at low-doses. Sixteen gene regulatory groups were selected in order to investigate murine hepatic gene expression changes following low-dose oral exposure to the beta-adrenergic agonist, isoproterenol (IPR). Gene sets in this focussed analysis included well-defined gene batteries and synexpression groups with co-regulated responses to toxin exposures and linkage of chronic responses to adverse outcomes. Significant changes of target gene expression within Nfkb, Stat3 and 5' terminal oligopryrimidine (5'TOP) batteries, as well as the acute phase and angiogenesis synexpression groups, were detected at IPR doses 100-fold lower than doses producing significant individual gene expression values. IPR-induced changes in these target gene groups were confirmed using a similar analysis of rat toxicogenomic data from published IPR-induced cardiotoxicity studies. Cumulative expression differences within gene sets were useful as aggregated metrics for benchmark dose calculations. The results supported the conclusion that toxicologically-relevant, co-regulated genes provide an effective means to reduce microarray dimensionality, thereby providing substantial statistical and interpretive power for quantitative analysis of low-dose, toxin-induced gene expression changes.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".