StratomeX: Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization

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dc.contributor.authorLex, Alexanderen_US
dc.contributor.authorStreit, Marcen_US
dc.contributor.authorSchulz, Hans-Joergen_US
dc.contributor.authorPartl, Christianen_US
dc.contributor.authorSchmalstieg, Dieteren_US
dc.contributor.authorPark, Peter J.en_US
dc.contributor.authorGehlenborg, Nilsen_US
dc.contributor.editorS. Bruckner, S. Miksch, and H. Pfisteren_US
dc.date.accessioned2015-02-28T07:02:27Z
dc.date.available2015-02-28T07:02:27Z
dc.date.issued2012en_US
dc.description.abstractIdentification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad-hoc scripts and static plots, which is inefficient and limits visual exploration of the data. To address this, we have developed StratomeX, an integrative visualization tool that allows investigators to explore the relationships of candidate subtypes across multiple genomic data types such as gene expression, DNA methylation, or copy number data. StratomeX represents datasets as columns and subtypes as bricks in these columns. Ribbons between the columns connect bricks to show subtype relationships across datasets. Drill-down features enable detailed exploration. StratomeX provides insights into the functional and clinical implications of candidate subtypes by employing small multiples, which allow investigators to assess the effect of subtypes on molecular pathways or outcomes such as patient survival. As the configuration of viewing parameters in such a multi-dataset, multi-view scenario is complex, we propose a meta visualization and configuration interface for dataset dependencies and data-view relationships. StratomeX is developed in close collaboration with domain experts.We describe case studies that illustrate how investigators used the tool to explore subtypes in large datasets and demonstrate how they efficiently replicated findings from the literature and gained new insights into the data.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume31
dc.identifier.doi10.1111/j.1467-8659.2012.03110.x
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2012.03110.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleStratomeX: Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterizationen_US
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