Coral: a web-based visual analysis tool for creating and characterizing cohorts

Adelberger, Patrick; Eckelt, Klaus; Bauer, Markus; Streit, Marc; Haslinger, Christian; Zichner, Thomas

doi:10.1093/bioinformatics/btab695

Cited by 3 publications

(10 citation statements)

References 8 publications

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“…For the cohort analysis use case, the expert noted that creating and labeling the groups becomes more and more tedious as the number increases. We discuss our plans to further support group creation in Section 8.2 and are additionally working on combining the presented approach with a dedicated tool for cohort creation and characterization [65].…”

Section: Domain Expert Feedbackmentioning

confidence: 99%

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt

Hinterreiter

Adelberger

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups is accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

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Section: Domain Expert Feedbackmentioning

confidence: 99%

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt

Hinterreiter

Adelberger

et al. 2023

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…Coral [2] is a cohort analysis tool specifically designed for creating and characterizing cohorts. However, the visual comparison of cohorts is limited to one or two attributes that need to be manually selected by the user.…”

Section: Related Workmentioning

confidence: 99%

“…In doing so, they neglect the vast combinatorial space opened up by high-dimensional data, from which further differences can be identified. Moreover, only Coral [2] allows comparison of more than two cohorts.…”

Section: Related Workmentioning

confidence: 99%

“…We have integrated Kokiri in Coral [2], a web-based visual analysis tool to create, refine, and characterize cohorts. After selecting the cohorts of interest, users can select one of multiple operations to apply.…”

Section: Integration In Coralmentioning

confidence: 99%

“…Kokiri integrated in Coral [2] comparing lung cancer patient cohorts of different race and gender. The ranked list A shows the most important attributes to differentiate the cohorts.…”

Section: Kokirimentioning

confidence: 99%

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Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts

Eckelt

Adelberger

Bauer

et al. 2022

Preprint

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We propose an interactive visual analytics approach for the characterization and comparison of patient subgroups (i.e., cohorts). Despite having the same disease and similar demographic characteristics, patients respond differently to therapy. One reason for this is the vast number of variables in the genome that influence the outcome. Nevertheless, most existing tools do not offer effective means to identify the most differing attributes or look at them in isolation, neglecting combinatorial effects. To fill this gap, we present Kokiri, a visual analytics approach that aims to separate cohorts based on user-selected data, ranks attributes by their importance to distinguish between cohorts, and visualizes the overlap and separability of the cohorts. Using our approach, users can additionally characterize the homogeneity and outliers of the cohort. To demonstrate the applicability of our approach, we integrated Kokiri in the Coral cohort analysis tool for the purpose of comparing and characterizing lung cancer patient cohorts.

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TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery

Zengin,

Masud,

Önal-Süzek

2024

Cancers

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For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database’s size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

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Coral: a web-based visual analysis tool for creating and characterizing cohorts

Cited by 3 publications

References 8 publications

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery

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