RegionScan: A comprehensive R package for region-level genome-wide association testing with integration and visualization of multiple-variant and single-variant hypothesis testing
Bibliographic record
Abstract
Abstract Summary RegionScan is an R package for comprehensive and scalable genome-wide association testing of region-level multiple-variant and single-variant statistics and visualization of the results. It implements various state-of-the-art region-level tests to improve signal detection under heterogeneous genetic architectures and facilitates comparison of multiple-variant region-level and single-variant test results. It exploits local linkage disequilibrium (LD) structure for genomic partitioning and LD-adaptive region definition. RegionScan is compatible with VCF input file formats for genotyped and imputed variants, and options are available for analysis of multi-allelic variants and unbalanced binary phenotypes. It accommodates parallel region-level processing and analysis to improve computational time and memory efficiency and provides detailed outputs and utility functions to assist results comparison, visualization, and interpretation. Availability and implementation RegionScan is freely available for download on GitHub ( https://github.com/brossardMyriam/RegionScan ). Contact bull@lunenfeld.ca , brossard@lunenfeld.ca . 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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.059 |
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".