GEPAT

GEPAT performs integrated analysis and biological interpretation of microarray gene expression data to identify expression patterns, differential expression, and pathway-level insights.

Key Features:

• Data Import and Normalization: Supports oligonucleotide and cDNA array data formats and provides multiple normalization methods for microarray datasets.
• Statistical Analysis Methods: Implements hierarchical clustering, k-means clustering, principal component analysis (PCA), linear model-based t-tests, and chromosomal profile comparisons.
• Biological Interpretation: Performs enrichment analyses for biological terms, pathway analysis, and interaction network exploration to link statistical results to biological context.
• Integration with Biological Databases: Incorporates various biological databases to provide detailed annotation for each probe on the microarray chip.
• Flexible Workflow: Allows selection of arbitrary subsets of probes or samples as starting points for iterative analyses and hypothesis testing.
• Scalability: Supports deployment on computer grids to accommodate large-scale microarray studies.

Scientific Applications:

• Disease Mechanism Studies: Analysis of gene expression changes to investigate molecular mechanisms underlying disease.
• Treatment Response Evaluation: Assessment of differential expression to evaluate responses to treatments or interventions.
• Developmental Biology: Exploration of gene expression dynamics during development.
• Gene Expression Regulation: Investigation of regulatory patterns and expression variability across conditions.
• Genetics and Molecular Biology Research: Derivation of pathway- and network-level insights from complex microarray datasets to inform genetics and molecular biology studies.

Methodology:

Computational methods explicitly include importing oligonucleotide and cDNA microarray formats, multiple normalization methods, hierarchical clustering, k-means clustering, principal component analysis (PCA), linear model-based t-tests, chromosomal profile comparisons, enrichment analyses, pathway analysis, interaction network exploration, probe annotation via biological databases, and deployment on computer grids.