Depower: An R Package for Simulation-Based Power Analysis of Differential Expression Studies

Area of Science:

• Biomedical Research
• Statistical Genetics
• Bioinformatics

Background:

• Sample size and power calculations are critical for robust experimental design in biomedical research.
• Current statistical tools often struggle with complex designs involving sample correlation and multiple testing for differential expression.
• Existing methodologies lack comprehensive software for power analysis in intricate biological studies.

Purpose of the Study:

• To introduce the R package depower, a novel software tool for power analysis in biomedical research.
• To provide a unified framework for sample size and power calculations accommodating complex experimental designs.
• To address the gap in statistical packages for differential expression studies with dependent and independent group comparisons.

Main Methods:

• Implementation of a simulation-based framework for power analysis.
• Development of the R package depower for practical application.
• Calculation of empirical null distributions to control false positive rates.

Main Results:

• The depower package offers a unified approach for power calculations in complex designs.
• The simulation-based framework effectively handles both independent and dependent group comparisons.
• The R package provides a solution for accommodating various sources of variability in differential expression studies.

Conclusions:

• The depower R package enhances the reliability of sample size and power calculations in biomedical research.
• This tool facilitates more accurate differential expression studies by accounting for complex design features.
• The package supports researchers in designing more robust and statistically sound experiments.