Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method
Bibliographic record
Abstract
Abstract Prospective birth cohorts offer the potential to interrogate the relation between early life environment and embedded biological processes such as DNA methylation (DNAme). These association studies are frequently conducted in the context of blood, a heterogeneous tissue composed of diverse cell types. Accounting for this cellular heterogeneity across samples is essential, as it is a main contributor to inter-individual DNAme variation. Integrated blood cell deconvolution of pediatric and longitudinal birth cohorts poses a major challenge, as existing methods fail to account for the distinct cell population shift between birth and adolescence. In this paper, we critically evaluated the reference-based deconvolution procedure and optimized its prediction accuracy for longitudinal birth cohorts using DNAme data from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. The optimized algorithm, CellsPickMe , integrates cord and adult references and picks DNA me features for each population of cells with machine learning algorithms. It demonstrated improved deconvolution accuracy in cord, pediatric, and adult blood samples compared to existing benchmark methods. CellsPickMe supports blood cell deconvolution across early developmental periods under a single framework, enabling cross-time-point integration of longitudinal DNAme studies. Given the increased resolution of cell populations predicted by CellsPickMe , this R package empowers researchers to explore immune system dynamics using DNAme data in population studies across the life course.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".