CroP

CroP visualizes time-varying relational biological data to enable analysis and interpretation of temporal patterns such as gene expression dynamics.

Key Features:

• Dynamic visualization models: Employs dynamic visualization models within flexible panels to represent evolving relational data.
• Simultaneous dataset analysis: Supports simultaneous upload and analysis of multiple datasets, including large-scale time series data.
• Clustering and time curve visualization: Provides clustering and time curve visualization to identify groups of data points and temporal patterns, including periodic waves of expression.
• Integration with public biomedical database for gene annotation: Integrates a public biomedical database to provide gene annotation and contextual information for genetic data.

Scientific Applications:

• Time-series gene expression analysis: Enables exploration and interpretation of temporal changes in gene expression datasets.
• Temporal pattern detection in biological networks: Facilitates detection of temporal behaviors and periodic expression waves across relational data.
• Relational data analysis across biological contexts: Supports analysis of evolving relationships within biological networks using annotated genetic information.

Methodology:

Uses coordinated panel visualization techniques leveraging dynamic models and flexible layouts, combined with clustering and time-curve visualization and integration of public biomedical gene annotation databases.