limmaGUI

limmaGUI performs differential expression analysis of two-color microarray data using limma's linear models and empirical Bayes variance moderation for statistical inference.

Key Features:

• Preprocessing: Supports background correction and normalization of two-color microarray intensities.
• Linear modeling and contrasts: Fits linear models and specifies contrasts to accommodate complex experimental designs and multiple RNA sources.
• Differential expression analysis: Computes differential expression statistics using limma's linear models and contrast framework.
• Empirical Bayes variance moderation: Applies empirical Bayes shrinkage of gene-wise residual variances to stabilize variance estimates.
• Quality-control and weighting: Incorporates quantitative spot quality weights, control spots, and within-array replicate spots into analysis.
• Multiple testing adjustment: Performs multiple testing adjustments on resulting test statistics.
• Integration with limma: Leverages the limma package's statistical framework for modeling and inference.

Scientific Applications:

• Gene expression profiling: Analysis of two-color microarray experiments to identify differentially expressed genes.
• Complex experimental designs: Studies that involve multiple RNA sources or multifactor experiments requiring contrasts and linear models.
• Comparative condition analysis: Identification of genes with altered expression across biological conditions or treatments.

Methodology:

Applies limma linear models and contrasts to microarray intensities, uses empirical Bayes shrinkage of gene-wise residual variances, and includes background correction, normalization, spot quality weighting, control-spot and within-array replicate handling, and multiple testing adjustments.