Gene Set Enrichment Analysis in Zebrafish Embryos Is Susceptible to False-Positive Results in the Absence of Differentially Expressed Genes
Bibliographic record
Abstract
High-throughput gene expression studies commonly employ pathway analyses to infer biological meaning from lists of differentially expressed genes (DEGs). In toxicology and pharmacology studies, treatment groups are analysed against vehicle controls to identify DEGs and altered pathways. Previously, we empirically quantified false-positive rates of DEGs in gene expression data from pools of vehicle-treated zebrafish embryos to determine appropriate study designs (sample and pool size). Here, the same data were subject to Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) to identify false-positive enriched pathways. As expected, the number of false-positive ORA results was lowest where pool and sample sizes were largest (conditions which also generated the fewest significant DEGs). In contrast, the frequency of GSEA false-positives generated through the fast GSEA (fgsea) algorithm increased with pool and sample size and was highest for simulations that generated 0 DEGs, with ribosomal gene sets significantly enriched with the highest frequency. We describe 2 distinct mechanisms by which GSEA generated these false-positive results, both of which are most likely to generate significant gene sets under conditions where expression differences are particularly low. Finally, GSEA analyses were repeated using 1 alternative GSEA algorithm (CERNO) and 11 different ranking statistics. In almost every analysis, the number of significant results was highest where pool size was highest, with ribosome as the more frequently enriched gene set, suggesting our observations to be generalizable to different implementations of GSEA. These results from zebrafish embryos suggest caution in interpreting any GSEA results in contrasts where there are no DEGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".