PolyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.

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

SUBMITTER: Eason K 

PROVIDER: S-EPMC5970540 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature