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Updated: May 24, 2025
Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
Published on: October 27, 2017
Gene Set Enrichment Analysis in Zebrafish Embryos Is Susceptible to False-Positive Results in the Absence of
John Dh Stead1, Hyojin Lee2, Andrew Williams3
1Department of Neuroscience, Carleton University, Ottawa, ON, Canada.
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.
Insights
Gene Set Enrichment Analysis (GSEA) can produce false-positive pathway enrichments, particularly in toxicology studies with few differentially expressed genes (DEGs). Researchers should exercise caution when interpreting GSEA results, especially when no DEGs are detected.
Area of Science:
- Toxicology
- Pharmacology
- Bioinformatics
- Gene Expression Analysis
Background:
- High-throughput gene expression studies utilize pathway analysis to interpret differentially expressed genes (DEGs).
- In toxicology and pharmacology, treatment groups are compared to vehicle controls to identify DEGs and altered pathways.
- Previous work quantified false-positive rates of DEGs in zebrafish embryos to guide study design.
Purpose of the Study:
- To identify false-positive enriched pathways using Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) in zebrafish embryo gene expression data.
- To investigate the impact of sample and pool size on false-positive rates in GSEA.
- To elucidate mechanisms driving GSEA false-positives and assess generalizability across algorithms and statistics.
Main Methods:
- Applied ORA and GSEA (specifically, the fast GSEA (fgsea) algorithm) to existing gene expression data from vehicle-treated zebrafish embryos.
- Analyzed data under varying sample and pool sizes, including conditions with zero DEGs.
- Repeated GSEA using an alternative algorithm (CERNO) and 11 different ranking statistics.
Main Results:
- False-positive ORA rates decreased with larger sample and pool sizes.
- GSEA false-positive rates, particularly with fgsea, increased with sample and pool size, peaking when zero DEGs were detected.
- Ribosomal gene sets were most frequently and falsely enriched in GSEA, especially under low-expression-difference conditions.
- These GSEA findings were consistent across different algorithms and ranking statistics.
Conclusions:
- GSEA can generate misleading pathway enrichments, especially in scenarios with minimal or no DEGs.
- The frequency of false-positive GSEA results is influenced by experimental design parameters like sample and pool size.
- Caution is advised when interpreting GSEA results in toxicological and pharmacological studies, particularly when contrasts yield no significant DEGs.
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