signatureSearch

signatureSearch performs gene expression signature (GES) searching and functional enrichment analysis (FEA) within R/Bioconductor, integrating existing and novel algorithms to compare query GES against large GES databases, support single-cell signature databases, and visualize drug-target networks.

Key Features:

• Gene Expression Signature Searching: Compares a query GES against extensive GES databases containing measurements from diverse genetic backgrounds, disease states, and drug perturbations to identify correlated signatures.
• Functional Enrichment Analysis: Applies specialized FEA methods to interpret which biological processes and pathways are modulated in GES search results.
• Visualization Tools: Provides visualization of GES search outcomes and FEA results, including drug-target network visualization to relate drugs to disease-like expression responses.
• Single Cell Analysis: Supports single-cell gene expression signature databases to identify novel network connections and distinct cell types.
• Customization and Flexibility: Supports standard and custom GES databases to tailor analyses to specific experimental datasets.
• Efficient Data Structures: Implements efficient data structures for robust performance with large-scale gene expression datasets.
• Algorithm Integration: Integrates existing and novel algorithms for GES searching and functional enrichment analysis within the R/Bioconductor framework.

Scientific Applications:

• Genetic Variation Analysis: Investigating genetic variations and their impact on cellular functions by comparing GES.
• Disease Mechanism Analysis: Analyzing disease mechanisms by comparing gene expression profiles between healthy and diseased states.
• Drug Response and Target Discovery: Exploring drug responses and identifying potential therapeutic targets through drug-target network analysis.
• Single-Cell Studies: Conducting single-cell studies to uncover novel biological insights at the cellular level.

Methodology:

Compares a query GES to extensive GES databases, applies functional enrichment analysis methods to search results, performs visualization including drug-target networks, supports single-cell GES database queries, and integrates existing and novel algorithms while using efficient data structures.