visMOP - A Visual Analytics Approach for Multi-omics Pathways

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dc.contributor.authorBrich, Nicolasen_US
dc.contributor.authorSchacherer, Nadineen_US
dc.contributor.authorHoene, Miriamen_US
dc.contributor.authorWeigert, Coraen_US
dc.contributor.authorLehmann, Raineren_US
dc.contributor.authorKrone, Michaelen_US
dc.contributor.editorBujack, Roxanaen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2023-06-10T06:16:50Z
dc.date.available2023-06-10T06:16:50Z
dc.date.issued2023
dc.description.abstractWe present an approach for the visual analysis of multi-omics data obtained using high-throughput methods. The term ''omics'' denotes measurements of different types of biologically relevant molecules, like the products of gene transcription (transcriptomics) or the abundance of proteins (proteomics). Current popular visualization approaches often only support analyzing each of these omics separately. This, however, disregards the interconnectedness of different biologically relevant molecules and processes. Consequently, it describes the actual events in the organism suboptimally or only partially. Our visual analytics approach for multi-omics data provides a comprehensive overview and details-on-demand by integrating the different omics types in multiple linked views. To give an overview, we map the measurements to known biological pathways and use a combination of a clustered network visualization, glyphs, and interactive filtering. To ensure the effectiveness and utility of our approach, we designed it in close collaboration with domain experts and assessed it using an exemplary workflow with real-world transcriptomics, proteomics, and lipidomics measurements from mice.en_US
dc.description.number3
dc.description.sectionheadersVisualization for Life Sciences
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14828
dc.identifier.issn1467-8659
dc.identifier.pages259-270
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14828
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14828
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing -> Graph drawings; Visualization techniques; Applied computing -> Bioinformatics
dc.subjectHuman centered computing
dc.subjectGraph drawings
dc.subjectVisualization techniques
dc.subjectApplied computing
dc.subjectBioinformatics
dc.titlevisMOP - A Visual Analytics Approach for Multi-omics Pathwaysen_US
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