PATH

PATH analyzes time-course high-dimensional genomic data to identify principal temporal trends and select genomic features underlying dynamic biological processes.

Key Features:

• Interactive Data Visualization: Provides visualization methods for exploring high-dimensional, time-resolved genomic data and temporal patterns.
• Dimension Reduction and Pattern Discovery: Applies Principal Trend Analysis (PTA) to reduce dimensionality while preserving principal temporal trends in time-course datasets.
• Feature Selection: Performs feature selection based on PTA to identify genomic features that drive observed temporal changes.
• Joint Principal Trend Analysis (JPTA): Implements Joint PTA for integrative analysis across multiple time-course datasets or experimental conditions.
• Comparison with Classical Methods: Supports benchmarking of PTA-based results against functional principal component analysis (FPCA).

Scientific Applications:

• Understanding Disease Progression: Analyzes temporal changes in gene expression and other genomic features to investigate molecular mechanisms of disease progression and treatment response.
• Developmental Biology Studies: Tracks gene expression dynamics over developmental time to identify key regulatory genes and pathways.
• Comparative Genomics: Enables integrative comparison across conditions or species using joint analysis to support evolutionary and comparative studies.

Methodology:

Principal Trend Analysis (PTA) is used to identify principal trends in time-course data; Joint PTA enables simultaneous analysis across multiple datasets; comparisons with functional principal component analysis (FPCA) are used for benchmarking.