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CIT: identification of differentially expressed clusters of genes from microarray data
Daniel R Rhodes1, Jeremy C Miller, Brian B Haab
1Laboratory of DNA and Protein Microarray Technology, Van Andel Research Institute, Grand Rapids, MI 49053, USA.
Bioinformatics (Oxford, England)
|February 12, 2002
Summary
The Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. It uses statistical methods to find gene clusters that distinguish between experimental groups, enhancing data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Microarray analysis is crucial for understanding gene expression patterns.
- Identifying differentially expressed genes is key to biological discovery.
- Existing tools may require integration for comprehensive analysis.
Purpose of the Study:
- To introduce the Cluster Identification Tool (CIT) for microarray data analysis.
- To describe CIT's methodology for identifying gene clusters.
- To highlight CIT's integration with CLUSTER and TREEVIEW.
Main Methods:
- CIT divides samples based on experimental parameters.
- It employs a statistical discrimination metric and permutation analysis.
- Identifies gene clusters or individual genes differentiating experimental groups.
Main Results:
- CIT effectively identifies differentially expressed genes.
- The tool pinpoints gene clusters that best discriminate between sample groups.
- Integration with CLUSTER and TREEVIEW provides a complete analysis package.
Conclusions:
- CIT is a valuable program for microarray analysis.
- It offers a robust method for identifying biologically significant gene clusters.
- The tool enhances the capabilities of existing microarray analysis software.