THEME

THEME analyzes loop-design microarray experiments to identify differentially expressed genes using linear-model-based hypothesis testing and to produce MIAME-compliant outputs suitable for GEO submission.

Key Features:

• Efficient DEG Identification: Constructs linear models and identifies differentially expressed genes (DEGs) from loop-design microarray data.
• User-Defined Statistical Tests: Supports user-defined statistical hypothesis tests by allowing modification of entries in the design matrix and contrast matrix.
• Multiple Statistical Tests: Applies multiple statistical tests for hypothesis evaluation and comparison.
• Cross-Validation of Results: Cross-validates DEG lists generated from multiple statistical tests to assess robustness.
• Data Assessment and Visualization: Provides integrated data assessment and visualization tools for evaluating microarray experiment performance.
• MIAME Compliance and GEO Submission: Generates MIAME-compliant experiment metadata and formats data for submission to the Gene Expression Omnibus (GEO).

Scientific Applications:

• Loop-design differential expression: Identifying gene expression changes in loop-design microarray experiments.
• Biological investigation: Investigating gene expression changes associated with diseases, developmental processes, or responses to environmental stimuli.

Methodology:

Constructs linear models, applies multiple statistical hypothesis tests, allows modification of the design matrix and contrast matrix, cross-validates DEG lists from multiple tests, provides data assessment and visualization, and formats MIAME-compliant metadata for GEO submission.