CORM: An R Package Implementing the Clustering of Regression Models Method for Gene Clustering

Area of Science:

• Bioinformatics
• Computational Biology
• Statistical Genetics

Background:

• Gene expression data analysis is crucial for understanding biological processes.
• Clustering methods are widely used to identify patterns in high-dimensional data.
• Existing methods may not fully capture complex relationships in gene expression.

Purpose of the Study:

• To introduce a novel R package for gene expression data clustering.
• To implement the clustering of regression models (CORM) method.
• To provide a user-friendly tool for gene expression analysis.

Main Methods:

• Development of an R package implementing the CORM method.
• Utilizing gene expression datasets for demonstration.
• Illustrating the functionality of each clustering algorithm within the package.

Main Results:

• A new R package, CORM, is now available.
• The package facilitates the clustering of genes based on regression models.
• Examples demonstrate the practical application of the package's functions.

Conclusions:

• The CORM R package offers a new approach to gene expression data clustering.
• The package provides accessible tools for researchers in bioinformatics and computational biology.
• Availability on CRAN ensures widespread adoption and use.