CrossICC utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering and it is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions for visualization and identifying subtypes of cancer. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes.
|Bioconductor views||BatchEffect Classification Clustering DifferentialExpression FeatureExtraction GUI GeneExpression GeneSetEnrichment Microarray Normalization Preprocessing RNASeq Software Survival Visualization|
|License||GPL-3 | file LICENSE|
|Package repository||View on GitHub|
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