Data repository for: Differential Expression Enrichment Tool (DEET): An interactive atlas of human differential gene expression
Bibliographic record
Abstract
Description adapted from manuscript abstract. RNA sequencing (RNA-seq) followed by differential gene expression analyses is a fundamental approach for making biological discoveries. Ongoing large-scale efforts to systematically process and normalize publicly available gene expression data facilitate rapid reanalyses of specific studies and the development of new methods for querying these public data. However, while several powerful tools can query systematically processed publicly available RNA-seq data at the individual sample level, there are fewer options for querying differentially expressed gene (DEG) lists generated from these experiments. Here, we present the Differential Expression Enrichment Tool (DEET), which allows users to interact with 3162 consistently processed DEG lists curated from 142 RNA-seq datasets obtained from the recount2 database, which contains data from consortiums (GTEx, TCGA) and individual labs (SRA). To establish DEET, we integrated systematically processed human RNA-seq data from recount2 with reported and previously predicted metadata from multiple sources. We then developed a CRAN R package (https://cran.r-project.org/web/packages/DEET/index.html) and Shiny App (https://wilsonlab-sickkids-uoft.shinyapps.io/DEET-shiny/) where users can compare their gene lists against the DEG lists within DEET. Here we present DEET and demonstrate how it can facilitate hypothesis generation and provide biological insight from user-defined differential gene expression results.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.300 | 0.182 |
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".