Unsupervised clustering of high-throughput gene expression data is widely 
adopted for cancer subtyping. However, cancer subtypes derived from a single 
dataset are usually not applicable across multiple datasets from different 
platforms. Merging different datasets is necessary to determine accurate and 
applicable cancer subtypes but is still embarrassing due to the batch effect. 
CrossICC is an R package designed for the unsupervised clustering of gene 
expression data from multiple datasets/platforms without the requirement of 
batch effect adjustment. CrossICC utilizes an iterative strategy to derive the 
optimal gene signature and cluster numbers from a consensus similarity matrix 
generated by consensus clustering. This package also provides abundant functions 
to visualize the identified subtypes and evaluate subtyping performance. We 
expected that CrossICC could be used to discover the robust cancer subtypes with 
significant translational implications in personalized care for cancer patients.
AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available 
at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor 
(http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL 
v3 License.