Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines, and tissue samples

Streit, Marc; Gratzl, Samuel; Stitz, Holger; Wernitznig, Andreas; Zichner, Thomas; Haslinger, Christian

doi:10.1101/277848

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…To demonstrate how existing visualization applications can be extended with KnowledgePearls retrieval capabilities, we implemented two prototypes: the first one is a Gapminder-inspired visualization implemented in Vega (see Figure 1) and the second one is our Ordino drug target discovery tool [44] that our collaborator used for the case study (see Section 7 and Figure 6).…”

Section: Methodsmentioning

confidence: 99%

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KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.

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Section: Methodsmentioning

confidence: 99%

“…We demonstrate the value and utility of the KnowledgePearls approach by integrating it into Ordino [44], a web-based discovery tool that allows users to flexibly rank and explore genes, cell lines, and tissue samples (see Figure 6).…”

Section: Case Studymentioning

confidence: 99%

KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Werner syndrome helicase is a selective vulnerability of microsatellite instability-high tumor cells

Lieb

Blaha-Ostermann

Kamper

et al. 2019

Preprint

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34Targeted cancer therapy is based on exploiting selective dependencies of tumor cells. By 35 leveraging recent large-scale genomic profiling and functional screening of cancer cell lines we 36 identified Werner syndrome helicase (WRN) as a novel specific vulnerability of microsatellite 37 instability-high (MSI-H) cancer cells. MSI, caused by defective mismatch repair is frequently 38 detected in human malignancies, in particular in colorectal, endometrial and gastric cancers. We 39 demonstrate that WRN inactivation selectively impairs the viability of MSI-H but not microsatellite 40 stable (MSS) colorectal and endometrial cancer cell lines. In MSI-H cells, WRN loss results in 41

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Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

et al. 2019

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Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

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Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines, and tissue samples

Cited by 3 publications

References 8 publications

KnowledgePearls: Provenance-Based Visualization Retrieval

KnowledgePearls: Provenance-Based Visualization Retrieval

Werner syndrome helicase is a selective vulnerability of microsatellite instability-high tumor cells

Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

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