Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations
Bibliographic record
Abstract
Intersample cellular heterogeneity (ISCH) is one of the largest contributors to DNA methylation (DNAme) variability. It is imperative to account for ISCH to accurately interpret analysis results in epigenome-wide association studies. We compiled this primer based on the current literature to guide researchers through the process of estimating and accounting for ISCH in DNA methylation studies. This primer outlines the procedure of bioinformatic ISCH prediction, including using reference-based and reference-free algorithms. It then follows with descriptions of several methods to account for ISCH in downstream analyses, including robust linear regression and principal-component-analysis-based adjustments. Finally, we outlined three methods for estimating differential DNAme signals in a cell-type-specific manner. Throughout the primer, we provided statistical and biological justification for our recommendations, as well as R code examples for ease of implementation. Intersample cellular heterogeneity (ISCH) is one of the largest contributors to DNA methylation (DNAme) variability. It is imperative to account for ISCH to accurately interpret analysis results in epigenome-wide association studies. We compiled this primer based on the current literature to guide researchers through the process of estimating and accounting for ISCH in DNA methylation studies. This primer outlines the procedure of bioinformatic ISCH prediction, including using reference-based and reference-free algorithms. It then follows with descriptions of several methods to account for ISCH in downstream analyses, including robust linear regression and principal-component-analysis-based adjustments. Finally, we outlined three methods for estimating differential DNAme signals in a cell-type-specific manner. Throughout the primer, we provided statistical and biological justification for our recommendations, as well as R code examples for ease of implementation.
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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 | 0.101 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".