Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Area of Science:

• Genomics
• Bioinformatics
• Statistical Genetics

Background:

• Gene set analysis (GSA) is crucial for interpreting genome-scale studies.
• Existing GSA methods often focus on differences in individual gene effects.
• Computational tools for GSA vary in their sensitivity to different biological signals.

Purpose of the Study:

• To develop a unifying framework for gene set analysis.
• To improve the performance and interpretability of GSA.
• To create a robust method less sensitive to sample size variations.

Main Methods:

• Developed a unifying framework for GSA based on effect size distributions.
• Implemented a meta-analysis-inspired approach to account for uncertainty in effect size estimates.
• Tested differences in effect size distributions between gene sets, considering proportions, sign, and magnitude of effects.

Main Results:

• The new GSA framework demonstrated significant performance gains over existing methods in simulations and real data.
• The approach enhances interpretability by defining statistical tests in terms of effect sizes.
• The method shows improved robustness to variations in sample sizes.

Conclusions:

• The proposed unifying framework offers a more powerful and interpretable approach to gene set analysis.
• This method advances the analysis of genome-scale data by providing robust insights into gene set perturbations.
• The framework's focus on effect size distributions represents a significant improvement in GSA methodology.