  Cancer is a number of related yet highly heterogeneous diseases. Correct
identification of cancer subtypes is critical for clinical decisions. The
advance in sequencing technologies has made it possible to study cancer based
on abundant genomics and transcriptomic (-omics) data. Such a data-driven
approach is expected to address limitations and issues with traditional methods
in identifying cancer subtypes. We evaluate the suitability of clustering--a
data mining tool to study heterogenous data when there is a lack of sufficient
understanding of the subject matters--in the identification of cancer subtypes.
A number of popular clustering algorithms and their consensus are explored, and
we find cancer subtypes identified by consensus clustering agree well with
clinical studies.
