  In cancer research, clustering techniques are widely used for exploratory
analyses and dimensionality reduction, playing a critical role in the
identification of novel cancer subtypes, often with direct implications for
patient management. As data collected by multiple research groups grows, it is
increasingly feasible to investigate the replicability of clustering
procedures, that is, their ability to consistently recover biologically
meaningful clusters across several datasets.
  In this paper, we review existing methods to assess replicability of
clustering analyses, and discuss a framework for evaluating cross-study
clustering replicability, useful when two or more studies are available. These
approaches can be applied to any clustering algorithm and can employ different
measures of similarity between partitions to quantify replicability, globally
(i.e. for the whole sample) as well as locally (i.e. for individual clusters).
Using experiments on synthetic and real gene expression data, we illustrate the
utility of replicability metrics to evaluate if the same clusters are
identified consistently across a collection of datasets.
