RNASeqDesign

RNASeqDesign optimizes sample size and sequencing depth and computes genome-wide statistical power for RNA-Seq experiments using pilot data.

Key Features:

• Discrete count modeling: Accounts for the discrete count nature of NGS RNA-Seq data distinct from fluorescence-based microarrays.
• Multi-dimensional constrained optimization: Treats study design as a multi-dimensional constrained optimization balancing sequencing depth and sample size under cost constraints.
• Pilot-data-driven power calculation: Uses pilot RNA-Seq data to perform power calculations and optimize design parameters such as sample size and sequencing depth.
• Mixture model fitting: Fits a mixture model to p-value distributions derived from pilot data to characterize signal and null components.
• Parametric bootstrap with approximated Wald statistics: Implements a parametric bootstrap procedure based on approximated Wald test statistics to infer genome-wide statistical power.
• Cost-aware design: Explicitly incorporates sequencing cost considerations when optimizing design parameters.
• Validation: Validated via simulations and real-world RNA-Seq applications.
• Practical design tasks: Supports five practical study design tasks for RNA-Seq experiments.

Scientific Applications:

• Power and sample size estimation: Design and power calculation for differential expression and other RNA-Seq analyses.
• Resource allocation optimization: Optimizing the trade-off between sequencing depth and sample size under budgetary constraints.
• Genome-wide transcriptomic studies: Planning genome-wide transcriptomic monitoring experiments using NGS RNA-Seq.
• Study validation and benchmarking: Benchmarking and validating study designs using simulations and real datasets in biomedical research projects.

Methodology:

Uses pilot RNA-Seq data; fits mixture models to p-value distributions; applies a parametric bootstrap procedure based on approximated Wald test statistics to perform power calculations and optimize sample size and sequencing depth while accounting for the discrete count nature of NGS data.