An empirically driven data reduction method on the human 450K methylation array to remove tissue specific non-variable CpGs
Bibliographic record
Abstract
BACKGROUND: Population based epigenetic association studies of disease and exposures are becoming more common with the availability of economical genome-wide technologies for interrogation of the methylome, such as the Illumina 450K Human Methylation Array (450K). Often, the expected small number of differentially methylated cytosine-guanine pairs (CpGs) in studies of the human methylome presents a statistical challenge, as the large number of CpGs measured on the 450K necessitates careful multiple test correction. While the 450K is a highly useful tool for population epigenetic studies, many of the CpGs tested are not variable and thus of limited information content in the context of the study and tissue. CpGs with observed lack of variability in the tissue under study could be removed to reduce the data dimensionality, limit the severity of multiple test correction and allow for improved detection of differential DNA methylation. METHODS: Here, we performed a meta-analysis of 450K data from three commonly studied human tissues, namely blood (605 samples), buccal epithelial cells (121 samples) and placenta (157 samples). We developed lists of CpGs that are non-variable in each tissue. RESULTS: These lists are surprisingly large (blood 114,204 CpGs, buccal epithelial cells 120,009 CpGs and placenta 101,367 CpGs) and thus will be valuable filters for epigenetic association studies, considerably reducing the dimensionality of the 450K and subsequently the multiple testing correction severity. CONCLUSIONS: We propose this empirically derived method for data reduction to allow for more power in detecting differential DNA methylation associated with exposures in studies on the human methylome.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".