DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity
Bibliographic record
Abstract
MOTIVATION: Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. RESULTS: To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. AVAILABILITY AND IMPLEMENTATION: The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.069 |
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".