DRCTdb: disease-related cell type analysis to decode cell type effect and underlying regulatory mechanisms
Bibliographic record
Abstract
Understanding the molecular mechanisms underlying genetic diseases is challenging due to environmental and genetic factors. Genome-wide association studies (GWAS) have identified numerous genetic loci, but their functional implications are largely unknown. Single-cell multiomics sequencing has emerged as a powerful tool to study disease-specific cell types and their relationship with genetic variants. However, comprehensive databases for exploring these mechanisms across different tissues are lacking. We present the Disease-Related Cell Type database (DRCTdb), integrating GWAS and single-cell multiomics data to identify disease-related cell types and elucidate their regulatory mechanisms. DRCTdb contains well-processed data from 16 studies, covering 4 million cells within 28 tissues. Users can browse relationships and regulatory mechanisms between SNPs of 42 genetic diseases and cell types based on GWAS and single-cell data. DRCTdb also offers data downloads and is available at https://singlecellatlas.top/DRCTDB . By integrating GWAS with single-cell data from 4 million cells across 28 tissues, DRCTdb database ( https://singlecellatlas.top/DRCTDB ) can help users explore disease-related cell types and regulatory mechanisms for 42 genetic diseases.
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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) | 0.000 | 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 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".