MA2C

MA2C performs model-based analysis of two-color microarray data to normalize probe intensities using probe GC content and detect peak genomic regions.

Key Features:

• GC-content normalization: Normalizes two-color microarray probe intensities using the GC content of probes to reduce systematic variability.
• Robust parameter estimation: Applies robust statistical methods for estimating model parameters within the analysis framework.
• Peak region detection algorithm: Implements a peak detection algorithm for identifying enriched genomic regions and has been reported to outperform other methods.
• Quality control visualization: Produces plots and visualizations for statistical quality control and interpretation of microarray results.

Scientific Applications:

• Gene Expression Analysis: Identifies differentially expressed genes by normalizing two-color microarray data and detecting expression peaks.
• Genomic Feature Mapping: Maps genomic features such as transcription factor binding sites and enhancers through precise peak detection.
• Comparative Genomics Studies: Compares gene expression profiles across species, tissues, or developmental stages using normalized microarray data.

Methodology:

MA2C applies a model-based computational framework that incorporates GC-content normalization of probes, robust statistical parameter estimation, a peak region detection algorithm, and generation of diagnostic plots.