Don’t brush off buccal data heterogeneity
Bibliographic record
Abstract
Buccal epithelial cells are among the most clinically accessible tissues and are increasingly being used to identify epigenetic disease patterns. However, substantial variation in buccal DNA methylation patterns indicates heterogeneity of cell types within and between samples, raising questions of data quality. We systematically estimated cell-type composition for a large collection of buccal and saliva samples from 11 published studies of DNA methylation. In these we identified numerous cases of buccal samples with questionable purity, which may be affected by sampling from individuals with neurodevelopmental disorders, and by the brushes used for sample collection. Further challenges are involved in comparisons with tissues such as saliva, in which buccal component varies widely. We propose a reference-based method of correcting for buccal purity that reduces unwanted variation while preserving cross-tissue differences. Our work demonstrates the wide variation of buccal quality in epigenetic studies and suggests a possible approach to overcome this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".