Gene Set Enrichment Analysis for Non-Monotone Association and Multiple Experimental Categories
Author Information
Author(s): Lin Rongheng, Dai Shuangshuang, Irwin Richard D, Heinloth Alexandra N, Boorman Gary A, Li Leping
Primary Institution: National Institute of Environmental Health Science
Hypothesis
Methods applicable to continuous non-monotone relationships are needed for gene set enrichment analysis.
Conclusion
The proposed framework captures both linear and non-linear associations between gene expression levels and phenotypic endpoints.
Supporting Evidence
- The study identified significant gene sets associated with liver injury across multiple experimental conditions.
- Results showed that the proposed R2 statistic effectively captures non-linear associations.
- Separate analyses for each experimental category were conducted to ensure accurate inference.
Takeaway
This study helps scientists understand how different genes are related to liver injury by looking at their expression levels in rats treated with various compounds.
Methodology
The study used natural cubic spline models to assess the relationship between gene expression and phenotypic endpoints, specifically focusing on liver injury indicators.
Potential Biases
Potential statistical dependency due to shared control groups in the experimental design.
Limitations
The study's findings may not generalize beyond the specific experimental conditions and compounds tested.
Participant Demographics
Rats (Rattus norvegicus, F344/N strain) were used in the study.
Statistical Information
P-Value
4.0 × 10^-7
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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