Development and Application of Laminar-specific Gene Expression Meta-analysis of the Human Neocortex
Bibliographic record
Abstract
Application of RNA sequencing (RNA-seq) has enabled the characterization of genome-wide gene expression in the human brain, including distinct layers of the neocortex. To advance our understanding of cortical architecture and layer-specific differences in gene expression, we performed a meta-analysis of multiple transcriptomic datasets of gene expression of varying techniques in neurotypical human brains. We observed differences in similarity between bulk-tissue and single-nuclei RNA-seq data, as well as layer-specific enrichment of gene expression. Using the techniques used for the meta-analysis, we developed a web application, LaminaRGeneVis, which allows users to perform similar analyses as performed here. Our results suggest that there is specific enrichment of genes that are relatively consistent across different types of data. These findings and the resulting application provide researchers a platform and reference point to better understand layer-specific gene expression in the human neocortex.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".