gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

Interactive visualization as a key tool for understanding medical omics data

Meeting Abstract

Suche in Medline nach

  • Federico Marini - University Medical Center Johannes Gutenberg University Mainz, Mainz, Germany
  • Charlotte Soneson - Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
  • Kevin Rue-Albrecht - Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
  • Aaron Lun - Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 64

doi: 10.3205/19gmds199, urn:nbn:de:0183-19gmds1991

Veröffentlicht: 6. September 2019

© 2019 Marini et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Life sciences have been evolving through the last decade to become a quantitative discipline, with a leading role played by high-throughput technologies (gene expression profiling, protein quantitation via mass spectrometry, high-throughput imaging).

Data is available in different experimental conditions, at different molecular layers, and also at different resolution, whereas single-cell techniques (especially in the field of transcriptomics) have enabled unprecedented views to understand complex phenomena by means of large, heterogeneous datasets.

How can interactive visualization help in this regard? It is an essential tool for quality assessment of often noisy multivariate data, for hypothesis generation and exploration, for the visualization of results, as well as for the efficient communication of findings.

The ideal visualization tool offers a variety of views on the data: reduced dimensionality views, features and samples plots for the assays and the metadata (scatter plots, heatmaps, distribution plots, interactive tables). Simultaneous and linked viewpoints are also fundamental to aid the exploration of complex data, and this holds true for anyone who accesses the data. A tool should also account for ways to guide external users (i.e. other practitioners) in obtaining new angles on the same input, to increase usability and impact of the data at hand.

We developed a general tool, iSEE ( [1]), for exploring a wide range of high dimensional datasets (bulk and single cell RNA-seq, mass cytometry, ...), with a solution that delivers scalability, flexibility, interactivity, and reproducibility, and I would like to present in this workshop how we addressed these aspects more in detail.

Using iSEE, inter-disciplinary efforts in understanding complex data are efficient in the many iterations commonly involved in exploration. Moreover, our tool can become a stepping stone to tackle the challenges of multi-omics approaches, by leveraging data structures able to accommodate the different biological, molecular, and clinical layers.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


Rue-Albrecht K, Marini F, Soneson C, Lun AT. iSEE – interactive summarizedexperiment explorer. F1000Research. 2018;7:741. DOI: 10.12688/f1000research.14966.1 Externer Link