pyPAGE

pyPAGE extends an information-theoretic framework to account for specific biases and artifacts in biological networks for gene-set enrichment analysis.

Key Features:

• Bias correction: Uses the concept of conditional information to address technical biases inherent to real-world biological network annotations.
• Regulon analysis: Detects non-random patterns of expression associated with pathways or regulons using information theory.
• Cell-type specific analysis: Supports analysis of single cell and spatial gene expression datasets.

Scientific Applications:

• Alzheimer's disease biomarker discovery: Identifies regulatory perturbations and differentially active regulons associated with Alzheimer's disease molecular etiology.
• Complex disease analysis: Enables precise and reliable discovery of gene expression programs in complex diseases.

Methodology:

pyPAGE applies conditional information within an information-theoretic framework to detect non-random patterns of expression associated with pathways or regulons while accounting for biases and artifacts in biological network annotations.