CARMAweb

CARMAweb performs comprehensive microarray data analysis using R and Bioconductor to preprocess, analyze, and interpret expression data from Affymetrix GeneChips, spotted two-color microarrays, and Applied Biosystems (ABI) microarrays.

Key Features:

• Platform support: Supports Affymetrix GeneChips, spotted two-color microarrays, and Applied Biosystems (ABI) microarrays.
• Data preprocessing: Performs background correction, quality control, and normalization of microarray data.
• Differential expression analysis: Detects differentially expressed genes using Bioconductor statistical routines.
• Cluster analysis: Groups similar expression profiles to identify patterns and relationships.
• Dimension reduction and visualization: Provides dimension reduction techniques and visualization tools to explore data structure and variability.
• Classification: Supports classification of samples based on gene expression profiles.
• Gene Ontology (GO) analysis: Performs GO-term analysis for gene annotation and functional interpretation.
• Reproducible reporting: Generates reports that document executed R commands using Sweave.
• R and Bioconductor integration: Uses R and Bioconductor functions for statistical analyses and data manipulation.

Scientific Applications:

• Differential expression discovery: Identification of genes with altered expression between conditions or treatments.
• Functional annotation: Interpretation of gene lists using Gene Ontology to reveal biological functions, processes, and cellular components.
• Sample classification: Assignment of samples to classes based on expression-profile–based classifiers.
• Pattern discovery: Detection of co-expressed gene clusters and expression patterns across samples.
• Data quality assessment: Assessment and correction of technical artifacts through background correction and normalization.

Methodology:

Uses R as the analytical engine with functions from the Bioconductor project for statistical analyses and data manipulation, and integrates Sweave to produce reports documenting executed R commands; the web application implementation is based on Java (J2EE).