RNASeqPowerCalculator

RNASeqPowerCalculator estimates statistical power and optimizes RNA-Seq experimental design for detecting differential expression under a negative binomial model.

Key Features:

• Power Analysis: Simulates RNA-Seq count data using parameters derived from six diverse public datasets, including cell line comparisons, tissue comparisons, and cancer studies, to calculate statistical power for paired and unpaired experiments.
• Performance Evaluation: Evaluates five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, EBSeq) across metrics including statistical power, ROC curves, AUC, Matthews correlation coefficient (MCC), and F-measures, identifying DESeq2 and edgeR as generally superior.
• Sample Size vs. Sequencing Depth: Quantifies how increasing sample size or sequencing depth affects power and shows that increasing sample size is more effective than increasing sequencing depth, particularly when depth exceeds 20 million reads.
• Gene Type Considerations: Reports that long intergenic noncoding RNAs (lincRNAs) have lower power than protein-coding mRNAs due to generally lower expression levels in RNA-Seq experiments.
• Paired-Sample Design: Demonstrates significant enhancement of statistical power through paired-sample RNA-Seq and emphasizes multifactor experimental design considerations.
• Budget-Constrained Optimization: Identifies a local optimal power achievable within budget constraints and shows sample size is the dominant factor influencing optimization relative to sequencing depth.

Scientific Applications:

• Experimental design planning: Guides allocation of sample size and sequencing depth for differential expression studies involving cell lines, tissues, and cancer types under budget constraints.
• Method benchmarking: Supports selection and comparison of differential expression analysis packages using power, ROC/AUC, MCC, and F-measure metrics.
• Design for specific targets: Informs design choices for studies targeting lincRNAs versus protein-coding mRNAs and for paired or multifactor experimental designs.

Methodology:

Simulates RNA-Seq count data under a negative binomial model using parameters from six public datasets and evaluates differential expression methods (DESeq, edgeR, DESeq2, sSeq, EBSeq) by computing statistical power, ROC curves, AUC, MCC, and F-measures.