pyiomica

pyiomica integrates, normalizes, annotates, and analyzes longitudinal multi-omics datasets to characterize temporal trends and dynamic biological patterns.

Key Features:

• Data Integration and Normalization: Integrates heterogeneous longitudinal omics datasets and performs normalization across experimental conditions and time points.
• Annotation and Categorization: Annotates and categorizes omics features to organize results by biological relevance.
• Visibility Graph Construction: Transforms time-series data into visibility graphs to represent temporal patterns as networks.
• Enrichment Analysis: Performs enrichment analysis to identify significantly enriched Gene Ontology terms and biological pathways.
• Graph Analysis: Performs computational network analyses to interpret complex temporal relationships.

Scientific Applications:

• Temporal Omics Studies: Analyzes dynamic molecular changes in studies of disease progression, treatment response, and developmental biology.

Methodology:

Implemented in Python and extending MathIOmica, employing normalization, visibility graph transformation, enrichment testing, and network-based analytical workflows.