Fulltext

Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study

Städler, Nicolas ; Dondelinger, Frank ; Hill, Steven M. ; Akbani, Rehan ; Lu, Yiling ; Mills, Gordon B. ; Mukherjee, Sach ; Sahinalp, Cenk ; Sahinalp, Cenk

In: Bioinformatics, 2017, vol. 33, no. 18, p. 2890-2896

Ajouter à la liste personnelle
    Titre
    • Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study
    Auteur
    • Städler, Nicolas. Global Pricing and Market Access, F. Hoffmann-La Roche Ltd, Basel, Switzerland
    • Dondelinger, Frank. Lancaster Medical School, Lancaster University, Lancaster, UK
    • Hill, Steven M.. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
    • Akbani, Rehan. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
    • Lu, Yiling. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
    • Mills, Gordon B.. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
    • Mukherjee, Sach. German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
    • Sahinalp, Cenk. Global Pricing and Market Access, F. Hoffmann-La Roche Ltd, Basel, Switzerland
    • Sahinalp, Cenk. Global Pricing and Market Access, F. Hoffmann-La Roche Ltd, Basel, Switzerland
    Type de document
    • Postprint
    Identifiant OAI-PMH
    • oai:doc.rero.ch:332901
    Summary
    Abstract Motivation Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging. Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset. Results We leverage recent ideas from high-dimensional statistics for testing and clustering in the network biology setting. The methods we describe can be applied directly to most continuous molecular measurements and networks do not need to be specified beforehand. We illustrate the ideas and methods in a case study using protein data from The Cancer Genome Atlas (TCGA). This provides evidence that patterns of interplay between signalling proteins differ significantly between cancer types. Furthermore, we show how the proposed approaches can be used to learn subtypes and the molecular networks that define them. Availability and implementation As the Bioconductor package nethet. Supplementary information Supplementary data are available at Bioinformatics online.