ASSIGN

ASSIGN evaluates pathway deregulation and activation in patient samples by adapting pathway signatures to specific cell- or tissue-contexts using Bayesian factor analysis to enable robust pathway activity profiling across heterogeneous genome-profiling platforms.

Key Features:

• Bayesian Factor Analysis Approach: Employs a Bayesian factor analysis framework to adapt predetermined pathway signatures derived from knowledge-based literature or perturbation experiments.
• Context-Specific Pathway Signatures: Transforms general pathway signatures into context-specific signatures tailored to controlled environments, patient samples, and primary tissues.
• Robustness Across Platforms: Accounts for systematic technical biases across multiple genome-profiling technologies to prioritize biological variation over technical noise.
• Simultaneous Profiling of Multiple Pathways: Models multiple correlated pathways concurrently to capture inter-pathway relationships in samples.
• Quantification of Deregulation/Activation Levels: Produces a score representing the extent of pathway deregulation or activation for each context-specific pathway in a sample.

Scientific Applications:

• Disease Mechanism Elucidation: Provides context-specific pathway activity profiles to support interpretation of disease-related molecular mechanisms.
• Personalized Therapeutics Guidance: Generates pathway activation scores that can inform disease- or condition-specific therapeutic decision-making.
• Simulated Data Analysis: Has been used to estimate pathway activities in simulated datasets to assess method performance.
• Cell Line Perturbation Studies: Applied to cell lines with perturbed pathways to quantify responses to stimuli or genetic modifications.
• Primary Tissue Samples: Applied to primary tissues, including breast carcinoma samples from The Cancer Genome Atlas and liver samples exposed to genotoxic carcinogens.

Methodology:

Adapts general pathway signatures into context-specific signatures using a Bayesian factor analysis framework, models multiple correlated pathways simultaneously, accounts for systematic technical biases across genome-profiling technologies, and quantifies pathway deregulation/activation as scores.