SSPA

SSPA performs sample size and power calculations to determine the number of biological replicates required to detect differentially expressed genes in microarray and next-generation sequencing (NGS) experiments.

Key Features:

• Pilot-data-based effect-size estimation: Estimates the density of effect sizes from pilot data rather than requiring user-specified fixed effect sizes, enabling inference from preliminary data.
• χ²-distributed test statistics: Performs power and sample size calculations for test statistics that follow a χ² distribution, applicable to a broad class of models.
• Support for high-dimensional generalized linear models: Applicable to high-dimensional generalized linear models commonly used in RNA-seq analysis.
• Support for general experimental designs: Accommodates more general experimental designs beyond simple two-group comparisons.

Scientific Applications:

• Microarray experiments: Determines the number of replicates needed to achieve adequate power for detecting differentially expressed genes in microarray studies.
• Next-generation sequencing (NGS) and RNA-seq: Provides sample size and power analysis for NGS studies, including RNA-seq, using methods suitable for high-dimensional models.

Methodology:

Estimates the density of effect sizes from pilot data and integrates these estimates into power and sample size calculations for χ²-distributed test statistics, including high-dimensional generalized linear models.