Datensatz:

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

Vorschaubild

Datum der Erstveröffentlichung

2024

Autor:innen

Bienroth, Denis
Charitakis, Natalie
Elliott, David
Porrello, Enzo R.
Ramialison, Mirana

Andere Beitragende

Repositorium der Erstveröffentlichung

Universitätsbibliothek Stuttgart

Version des Datensatzes

DOI (Link zu den Daten)
https://doi.org/10.18419/darus-4254
Link zur Lizenz
oops

Angaben zur Forschungsförderung

Deutsche Forschungsgemeinschaft (DFG): 251654672

Projekt

Sammlungen

Informatik und Informationswissenschaft: Datensätze
Core Facility der Universität Konstanz
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Gesperrt bis

Titel in einer weiteren Sprache

Publikationsstatus
Published

Zusammenfassung

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

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Computer and Information Science, Medicine, Health and Life Sciences, Spatial Transcriptomics, Virtual Reality, Information Visualization, Computer Science

Zugehörige Publikationen in KOPS

Link zu zugehöriger Publikation
https://doi.org/10.1101/2023.03.31.535025
Link zu zugehörigem Datensatz
https://doi.org/10.18419/darus-4254/1

Zitieren

ISO 690BIENROTH, 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)
BibTex
RDF
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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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