title: CoINcIDE: A framework for discovery of patient subtypes across multiple datasets.
abstract: Patient disease subtypes have the potential to transform personalized medicine. However, many patient subtypes derived from unsupervised clustering analyses on high-dimensional datasets are not replicable across multiple datasets, limiting their clinical utility. We present CoINcIDE, a novel methodological framework for the discovery of patient subtypes across multiple datasets that requires no between-dataset transformations. We also present a high-quality database collection, curatedBreastData, with over 2,500 breast cancer gene expression samples. We use CoINcIDE to discover novel breast and ovarian cancer subtypes with prognostic significance and novel hypothesized ovarian therapeutic targets across multiple datasets. CoINcIDE and curatedBreastData are available as R packages.
authors.affiliation: The Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305, USA.
authors.forename: Catherine R
authors.initials: CR
authors.lastname: Planey
authors.affiliation: The Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305, USA. ogevaert@stanford.edu.
authors.forename: Olivier
authors.initials: O
authors.lastname: Gevaert
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D066264:Datasets as Topic; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000235:genetics; D011379:Prognosis; D012372:ROC Curve; D012984:Software