
The analysis of whole genomes of pan‐cancer data sets provides a challenge for researchers. This study contributes to the literature concerning identifying robust subgroups with clear biological interpretation. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model; the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions; and the transparent biological understanding of the identified clusters, since each cluster is characterized by its top‐ranked genomic features. This method allows the identification of robust and biologically meaningful clusters of pan‐cancer samples.
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