IPBT

IPBT applies a Bayesian framework to derive informative priors from public microarray datasets to improve detection of differentially expressed genes in high-throughput 'large p, small n' experiments.

Key Features:

• Informative priors derivation: Derives informative priors from existing public microarray datasets and historical data repositories.
• Bayesian framework: Incorporates derived priors into Bayesian testing for differential expression.
• Public microarray integration: Leverages data produced on consistent platforms and protocols to construct priors.
• Addresses large p, small n: Targets scenarios where the number of variables (p) greatly exceeds the number of samples (n) by integrating genomics big data into inference.
• Validation and benchmarking: Demonstrates improvement over traditional methods, including Bayesian hierarchical model–based approaches, via simulation studies and real-world data analyses.
• Implementation: Provided as an R package implementation.

Scientific Applications:

• Differential expression analysis (microarray): Detection of differentially expressed genes using microarray data with enhanced statistical power from informative priors.
• High-throughput experiments with limited samples: Statistical inference for high-throughput biotechnologies subject to 'large p, small n' constraints.
• Leveraging historical genomics data: Integration of historical/public microarray repositories into current analyses to improve inference.
• Method benchmarking: Comparative assessment of Bayesian and hierarchical model–based approaches using simulations and empirical datasets.

Methodology:

IPBT derives informative priors from public microarray datasets and incorporates them into a Bayesian differential expression testing framework; validation used simulation studies and analyses of real-world microarray data, and the methods are implemented in R.