Weighted Trigonometric Regression for Suboptimal Designs in Circadian Transcriptome Studies
Bibliographic record
Abstract
ABSTRACT Circadian transcriptome studies often use trigonometric regression to model gene expression over time. Ideally, protocols in these studies would collect tissue samples at evenly distributed and equally spaced time points over a 24‐hour period. This sample collection protocol is known as an equispaced design, which is considered the optimal experimental design for trigonometric regression under multiple statistical criteria. However, implementing equispaced designs in studies involving individuals is logistically challenging, and failure to employ an equispaced design could introduce variability in the statistical power of a hypothesis test relative to a model's phase‐shift parameter estimates. This article is motivated by the variability in power for hypothesis testing when tissue samples are not collected under an equispaced design, and considers a weighted trigonometric regression as a remedy. Specifically, the weights for this regression are the normalized reciprocals of estimates derived from a kernel density estimator for sample collection time, which inflates the weight of samples collected at underrepresented time points. A search procedure is also introduced to identify the hyperparameter for kernel density estimation that relates to maximizing the smallest eigenvalue of the Hessian of weighted squared loss, which is motivated by the ‐optimality criterion from experimental design literature. Simulation studies consistently demonstrate that this weighted regression mitigates variability in power for hypothesis tests performed with an estimated model. Illustrations with six circadian transcriptome datasets further indicate that this weighted regression consistently yields larger test statistics than its unweighted counterpart for first‐order trigonometric regression, or cosinor regression, which is prevalent in circadian transcriptome studies.
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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 | 0.051 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".