RNASeqDesign: A framework for RNA-Seq genome-wide power calculation and study design issues.

Area of Science:

• Genomics
• Bioinformatics
• Statistical Genetics

Background:

• Next-generation sequencing (NGS) and RNA sequencing (RNA-Seq) are powerful for transcriptomic analysis.
• High costs and complexity remain barriers for RNA-Seq adoption in biomedical research.
• RNA-Seq data requires discrete count modeling, unlike microarray data.

Purpose of the Study:

• To develop a statistical framework, RNASeqDesign, for optimizing RNA-Seq experimental design.
• To enable power calculations and determine optimal sample size and sequencing depth using pilot data.
• To address the multi-dimensional optimization problem in RNA-Seq study design.

Main Methods:

• Utilized pilot RNA-Seq data for power calculation.
• Employed mixture model fitting for p-value distribution.
• Applied a parametric bootstrap procedure with approximated Wald test statistics.
• Inferred genome-wide power for sample size and sequencing depth optimization.

Main Results:

• Developed and validated the RNASeqDesign statistical framework.
• Demonstrated practical application through five study design tasks.
• Evaluated performance via simulations and three real-world RNA-Seq applications.
• Provided a method to optimize RNA-Seq design considering cost constraints.

Conclusions:

• RNASeqDesign offers a robust approach for RNA-Seq study design.
• The framework effectively optimizes sample size and sequencing depth, balancing cost and statistical power.
• Facilitates more efficient and cost-effective transcriptomic research.