GeneExpressionSignature

GeneExpressionSignature analyzes gene expression signatures to identify functional connections among biological events and facilitate applications such as drug repurposing.

Key Features:

• Rank-Merging Algorithms: Implements two rank-merging algorithms to integrate gene expression data from diverse sources.
• Similarity-Scoring Algorithms: Includes two similarity-scoring algorithms to assess correlation between gene expression signatures and detect similarities among biological entities such as drugs and diseases.
• Nonparametric, Rank-Based Pattern-Matching: Employs a nonparametric, rank-based pattern-matching approach grounded in the Kolmogorov-Smirnov statistic to quantify distances between signatures without distributional assumptions.

Scientific Applications:

• Drug Repurposing: Comparing gene expression signatures associated with drugs and diseases to identify potential new uses for existing medications.
• Biological State Analysis: Correlating gene expression with specific biological states to explore cellular responses, disease mechanisms, and therapeutic targets.

Methodology:

Implements two rank-merging algorithms, two similarity-scoring algorithms, and a nonparametric, rank-based pattern-matching method based on the Kolmogorov-Smirnov statistic for quantifying signature similarity.