TimeClust

TimeClust clusters genes by their temporal expression profiles from DNA microarray time-course experiments to identify co-expression patterns in short time series.

Key Features:

• Two original algorithms: Implements two novel algorithms specifically developed to cluster short time series gene expression data.
• DNA microarray time-course support: Operates on temporal expression data derived from DNA microarray time-course experiments.
• Hierarchical clustering: Incorporates hierarchical clustering to explore gene expression patterns at multiple levels of granularity.
• Self-Organizing Maps (SOMs): Integrates SOMs to map high-dimensional gene expression profiles onto a two-dimensional grid for pattern identification.

Scientific Applications:

• Gene Expression Profiling: Analyzes temporal changes in gene expression to reveal dynamic regulatory patterns.
• Pathway Analysis: Groups genes with similar temporal profiles to aid identification of co-regulated pathways and interactions.
• Disease Research: Compares time-course expression between diseased and healthy states to support biomarker and therapeutic target discovery.

Methodology:

Applies two original clustering algorithms tailored for short time series alongside hierarchical clustering and self-organizing maps to cluster temporal gene expression from DNA microarray time-course data.