A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Brain cellular heterogeneity may bias cell type specific DNA methylation patterns, influencing findings in psychiatric epigenetic studies. We performed fluorescence activated cell sorting (FACS) of neuronal nuclei and Illumina HM450 DNA methylation profiling in post mortem frontal cortex of 29 major depression and 29 matched controls. We identify genomic features and ontologies enriched for cell type specific epigenetic variation. Using the top cell epigenotype specific (CETS) marks, we generated a publically available R package, "CETS," capable of quantifying neuronal proportions and generating in silico neuronal profiles from DNA methylation data. We demonstrate a significant overlap in major depression DNA methylation associations between FACS separated and CETS model generated neuronal profiles relative to bulk profiles. CETS derived neuronal proportions correlated significantly with age in the frontal cortex and cerebellum and accounted for epigenetic variation between brain regions. CETS based control of cellular heterogeneity will enable more robust hypothesis testing in the brain.
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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 | 0.000 | 0.000 |
| 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 it