pcaGoPromoter

pcaGoPromoter applies principal component analysis to genome-wide gene expression data to identify major axes of variation and associate principal components with biological functions and regulatory signals.

Key Features:

• Principal Component Analysis (PCA): Performs PCA to provide an overview of gene expression datasets, detect patterns and groupings, and reduce dimensionality.
• Dimensionality reduction for large datasets: Surveys multivariate differences across large genome-wide expression datasets to highlight significant variation between experimental conditions.
• Functional Interpretation: Links principal component dimensions to biological functions using gene ontology (GO) terms for functional annotation of PCs.
• Regulatory Insights: Predicts transcription factor binding sites and interprets them via overrepresentation analysis using the primo algorithm.
• Robustness Evaluation: Incorporates cross-validation techniques to assess the robustness of results derived from PCA and downstream interpretations.
• Visualization Tools: Generates plots of PCA scores and associated GO terms to visualize relationships between samples, components, and functions.
• Platform Compatibility: Supports datasets using gene symbols or Entrez IDs as probe identifiers and provides specific support for several Affymetrix GeneChip platforms, including the Affymetrix Human Genome U133 Plus 2.0 chip.

Scientific Applications:

• Exploratory analysis of gene expression: Detects sample groupings and major sources of variation in genome-wide expression studies.
• Functional annotation of variation: Associates principal components with GO terms to interpret biological processes underlying expression variation.
• Regulatory mechanism inference: Identifies overrepresented transcription factor binding sites to suggest regulatory drivers of expression changes using the primo algorithm.
• Method validation: Uses cross-validation to evaluate the stability and reliability of PCA-derived interpretations.
• Case study analysis: Applied to Affymetrix Human Genome U133 Plus 2.0 serum stimulation data to distinguish control versus serum- or inhibitor-treated samples and reveal processes such as cell cycle progression and cholesterol synthesis.

Methodology:

Performs principal component analysis, links PC dimensions to gene ontology terms via overrepresentation/enrichment analysis, predicts transcription factor binding sites using the primo algorithm with overrepresentation analysis, applies cross-validation for robustness assessment, and plots PCA scores with associated GO terms.