Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics (Software and Data)

Kurzanzeige
dc.contributor.authorBienroth, Denis
dc.contributor.authorCharitakis, Natalie
dc.contributor.authorJaeger-Honz, Sabrina
dc.contributor.authorGarkov, Dimitar
dc.contributor.authorElliott, David
dc.contributor.authorPorrello, Enzo R.
dc.contributor.authorKlein, Karsten
dc.contributor.authorNim, Hieu T.
dc.contributor.authorSchreiber, Falk
dc.contributor.authorRamialison, Mirana
dc.date.accessioned2025-02-14T10:37:27Z
dc.date.available2025-02-14T10:37:27Z
dc.date.created2024-06-10T09:03:59Z
dc.date.issued2024
dc.description.abstractHere, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible. VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.htmleng
dc.description.versionpublisheddeu
dc.identifier.doi10.18419/darus-4254
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/72334
dc.language.isoeng
dc.relation.haspart10.18419/darus-4254/1
dc.relation.haspart10.18419/darus-4254/2
dc.relation.haspart10.18419/darus-4254/3
dc.relation.isreferencedby10.1101/2023.03.31.535025
dc.subjectComputer and Information Science
dc.subjectMedicine, Health and Life Sciences
dc.subjectSpatial Transcriptomics
dc.subjectVirtual Reality
dc.subjectInformation Visualization
dc.subjectComputer Science
dc.subject.ddc004
dc.titleSpatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics (Software and Data)eng
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kops.citation.bibtex
kops.citation.iso690BIENROTH,  Denis,  Natalie CHARITAKIS,  Sabrina JAEGER-HONZ,  Dimitar GARKOV,  David ELLIOTT,  Enzo R. PORRELLO,  Karsten KLEIN,  Hieu T. NIM,  Falk SCHREIBER,  Mirana RAMIALISON, 2024. Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics (Software and Data)deu
kops.citation.iso690BIENROTH,  Denis,  Natalie CHARITAKIS,  Sabrina JAEGER-HONZ,  Dimitar GARKOV,  David ELLIOTT,  Enzo R. PORRELLO,  Karsten KLEIN,  Hieu T. NIM,  Falk SCHREIBER,  Mirana RAMIALISON, 2024. Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics (Software and Data)eng
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    <dcterms:abstract xml:lang="eng">Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". 

Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible.  

VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html</dcterms:abstract>
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