Gene Expression Variability as a Unifying Element of the Pluripotency Network
Bibliographic record
Abstract
Heterogeneity is a hallmark of stem cell populations, in part due to the molecular differences between cells undergoing self-renewal and those poised to differentiate. We examined phenotypic and molecular heterogeneity in pluripotent stem cell populations, using public gene expression data sets. A high degree of concordance was observed between global gene expression variability and the reported heterogeneity of different human pluripotent lines. Network analysis demonstrated that low-variability genes were the most highly connected, suggesting that these are the most stable elements of the gene regulatory network and are under the highest regulatory constraints. Known drivers of pluripotency were among these, with lowest expression variability of POU5F1 in cells with the highest capacity for self-renewal. Variability of gene expression provides a reliable measure of phenotypic and molecular heterogeneity and predicts those genes with the highest degree of regulatory constraint within the pluripotency network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".