SUMMARY: Identifying molecular cancer subtypes from multi-omics data is an 
important step in the personalized medicine. We introduce CancerSubtypes, an R 
package for identifying cancer subtypes using multi-omics data, including gene 
expression, miRNA expression and DNA methylation data. CancerSubtypes integrates 
four main computational methods which are highly cited for cancer subtype 
identification and provides a standardized framework for data pre-processing, 
feature selection, and result follow-up analyses, including results computing, 
biology validation and visualization. The input and output of each step in the 
framework are packaged in the same data format, making it convenience to compare 
different methods. The package is useful for inferring cancer subtypes from an 
input genomic dataset, comparing the predictions from different well-known 
methods and testing new subtype discovery methods, as shown with different 
application scenarios in the Supplementary Material.
AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available 
under GPL-2 license from the Bioconductor website 
(http://bioconductor.org/packages/CancerSubtypes/).
CONTACT: thuc.le@unisa.edu.au or jiuyong.li@unisa.edu.au.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.