biostudies-literatureEason KEngineering and Physical Sciences Research Council182https://www.ebi.ac.uk/biostudies/studies/S-EPMC5970540biostudies-literatureUnknown19(1)<h4>Background</h4>To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce "polyClustR", a tool that reconciles clusters identified by different methods into subtype "communities" using a hypergeometric test or a measure of relative proportion of common samples.<h4>Results</h4>The polyClustR pipeline was initially tested using a breast cancer dataset to demonstrate how results are compatible with and add to the understanding of this well-characterised cancer. Two uveal melanoma datasets BMC bioinformaticspolyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.PMC5970540EP/J500240/1Sadanandam AEason KNyamundanda GfalsepolyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.<h4>Background</h4>To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce "polyClustR", a tool that reconciles clusters identified by different methods into subtype "communities" using a hypergeometric test or a measure of relative proportion of common samples.<h4>Results</h4>The polyClustR pipeline was initially tested using a breast cancer dataset to demonstrate how results are compatible with and add to the understanding of this well-characterised cancer. Two uveal melanoma datasets 2018-01-01T00:00:00Z2018 May2026-04-29T12:31:39.733Z2019-03-26T23:39:32ZS-EPMC59705402980143310.1186/s12859-018-2204-4