CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data
Işık Barış Fidaner1, Ayca Cankorur-Cetinkaya2, Duygu Dikicioglu2
1Department of Computer Engineering.
Bioinformatics (Oxford, England)
|September 29, 2015
Summary
CLUSTERnGO is a new bioinformatics tool for analyzing time-series data, excelling with transient phenomena. It uses advanced clustering to reveal novel biological insights and outperforms existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Statistical Analysis
Background:
- Standard bioinformatics tools struggle with transient phenomena in time-series data.
- This limitation hinders the extraction of meaningful biological information.
- There is a need for specialized, user-friendly tools for time-series analysis.
Purpose of the Study:
- To introduce CLUSTERnGO, a novel statistical application for time-series data analysis.
- To address the limitations of existing tools in handling transient phenomena.
- To provide a flexible and user-friendly solution for biological data interpretation.
Main Methods:
- Development of CLUSTERnGO, a model-based clustering application.
- Utilizes an Infinite Mixture of Piecewise Linear Sequences (Bayesian non-parametric model).
- Employs a novel Two-Stage Clustering methodology and Gene Ontology (GO) enrichment analysis with multiple hypothesis testing.
Main Results:
- CLUSTERnGO successfully analyzes time-series datasets, including transient phenomena.
- The application outperforms existing algorithms in assigning unique GO term enrichments.
- Novel biological insights were uncovered in diverse test cases, surpassing original publication findings.
Conclusions:
- CLUSTERnGO offers a powerful and flexible approach to time-series data analysis.
- The tool enhances biological interpretation through GO enrichment.
- It provides valuable insights for both specialist and non-specialist users.
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