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Gene expression profiles are essential in identifying different cancerphenotypes. Clustering gene expression datasets can provide accurateidentification of cancerous cell lines, but this task is challenging due to thesmall sample size and high dimensionality. Using the $K$-means clusteringalgorithm we determine the organisation of the solution space for a variety ofgene expression datasets using energy landscape theory. The solution spacelandscapes allow us to understand $K$-means performance, and guide moreeffective use when varying common dataset properties; number of features,number of clusters, and cluster distribution. We find that the landscapes havea single-funnelled structure for the appropriate number of clusters, which islost when the number of clusters deviates from this. We quantify this landscapestructure using a frustration metric and show that it may provide a noveldiagnostic tool for the appropriate number of cancer subtypes.

Archetypal solution spaces for clustering gene expression datasets in identification of cancer subtypes