Data from: Single cell RNA-seq data of E18 fetal thymocytes from HEB Vav-iCre and Id3-KO mice and their wild type littermate counterparts
Bibliographic record
Abstract
γδ T cells that produce IL-17 (γδT17) play essential roles in barrier immunity and autoimmunity, but the gene networks that install their functions are not well understood. To understand how HEB and Id3 regulate γδT17 cell development, we conducted single-cell RNA-sequencing on fetal thymic γδ T cells from Tcf12-deficient (HEB cKO) and Id3 knockout (Id3-KO) E18 embryos. Four datasets were generated. The first two consist of WT and HEB cKO datasets derived from sorted gamma-delta T cells. The second two consist of WT and Id3-KO datasets derived from magnetically enriched for CD4/CD8-negative cells. HEB datasets were generated with 10X Genomics 5' chemistry, and Id3 datasets were generated with 10X Genomics 3' chemistry. Raw sequence files were processed using Cell Ranger to generate matrix files, which have been uploaded here. We also include R-markdown files (RMD) and R-markdown HTML output files to provide code and programs used for the data analysis shown in the associated paper.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.034 |
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".