timeClip

timeClip performs topology-based pathway analysis on long time-series gene expression datasets lacking replicates to identify time-dependent pathways and their most time-dependent segments.

Key Features:

• Topology-Based Analysis: Performs pathway topology-aware analysis to increase detection power relative to traditional enrichment methods.
• Dimension Reduction Techniques: Employs dimension reduction to manage high-dimensional time-series gene expression data without replicates.
• Graph Decomposition Theory: Uses graph decomposition theory to dissect pathways into constituent parts for pinpointing time-dependent segments.
• Two-Step Analysis Process: Implements a two-step process that first selects time-dependent pathways and then highlights the most time-dependent portions within those pathways.

Scientific Applications:

• Benchmark study (mouse muscle regeneration): Identified 76 time-dependent pathways in a mouse muscle regeneration dataset.
• mTOR signaling pathway analysis: Revealed temporal events including early activation via growth factor signals and subsequent protein production bursts required for fiber regeneration.
• Simulated and real-world datasets: Applied to both simulated and empirical datasets to demonstrate efficacy in detecting time-dependent pathway behavior.

Methodology:

Performs topology-based pathway analysis combined with dimension reduction and graph decomposition theory in a two-step workflow of selecting time-dependent pathways and then highlighting their most time-dependent portions.