{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Eason K"],"funding":["Engineering and Physical Sciences Research Council"],"pagination":["182"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5970540"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(1)"],"pubmed_abstract":["Background
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.Results
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 "],"journal":["BMC bioinformatics"],"pubmed_title":["polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance."],"pmcid":["PMC5970540"],"funding_grant_id":["EP/J500240/1"],"pubmed_authors":["Sadanandam A","Eason K","Nyamundanda G"],"additional_accession":[]},"is_claimable":false,"name":"polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.","description":"Background
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.Results
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 ","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 May","modification":"2026-04-29T12:31:39.733Z","creation":"2019-03-26T23:39:32Z"},"accession":"S-EPMC5970540","cross_references":{"pubmed":["29801433"],"doi":["10.1186/s12859-018-2204-4"]}}