R functions for cDNA array analysis

R functions for cDNA array analysis provide a suite of R functions to process and analyze cDNA microarray data, performing quality-based filtering, normalization between fluorescent labels, and Bayesian hierarchical modeling with MCMC for inference of gene expression differences.

Key Features:

• Quality index computation: Computes a quality index from duplicate spots on the same slide to identify and filter out outliers, poor-quality genes, and problematic slides.
• Calibration experiments: Demonstrates and accounts for the necessity of normalization between fluorescent labels, noting that normalization is slide-dependent and non-linear.
• Rank invariant method: Selects non-differentially expressed genes and constructs normalization curves for comparative experiments.
• Hierarchical Bayesian models: Incorporates multiple levels of variation to assess the significance of gene effects in comparative experiments.
• MCMC procedures: Employs Markov Chain Monte Carlo methods to perform Bayesian hierarchical modeling and provide statistical inference.

Scientific Applications:

• Escherichia coli comparative expression analysis: Applied to two experimental groups of Escherichia coli grown under glucose and acetate conditions with datasets of 125 and 4129 genes.
• Small- and large-scale gene expression studies: Applicable to both small-scale (e.g., 125 genes) and large-scale (e.g., 4129 genes) cDNA microarray experiments.

Methodology:

Computational methods include quality-index filtering based on duplicate spots, slide-dependent non-linear normalization between fluorescent labels, a rank invariant method to select non-differentially expressed genes and construct normalization curves, hierarchical Bayesian models incorporating multiple levels of variation, and MCMC procedures for Bayesian inference.