# PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations
Figure 1 shows PhenoStacks, a visualization system for cross-sectional phenotype comparison built by Glueck et al., it aims to support the exploration of phenotype variation within and between cross-sectional patient cohorts. The authors work with four genetics researchers for design, implementation and testing.
Phenotypes are observable and measurable patient traits primarily caused by genetic variation. Analyses of phenotypes can help understand how phenotype vary across patients with genetic diseases, both within and between cohorts.
The authors interview genetics researchers and design an algorithm to simplify ontology topologies for visualization. Common tasks performed by the researchers can be easily done using PhenoStacks. The authors also use an "opt-out" approach which performs all the tasks and presents the results by default.
Figure 1 shows an overview of PhenoStacks. Section A shows a sunburst chart that visualizes summarized global patterns. Section B and C show groups of phenotypes of the selected ontology. Section D shows a matrix mapping patients in a cohort with phenotypes in Section C. Section E shows a search bar for searching the results.
Data preparation is done using Python with a data model based on the cohort subgraph shown in Figure 2 and Figure 3.
Glueck et al. identify Visualization of Genetics Data as the first relevant area in developing PhenoStacks, existing systems such as cBio and CircleMap use a compact color-coded matrix to represent genomic values. Savant 2, Circos  and MizBee  use space-filling layouts based on genome coordinates. Cytoscape  and VisANT  use network representations for visualizing gene pathways.
The second relevant area is Visualizations of Cohorts, the authors review a survey on visualization systems for electronic health records by Rind et al. . The authors also review COQUITO that analyzes temporal constraints of cohort membership. CAVA explores temporal events between cohorts and CoCo compares temporal events between cohorts.
Ontologies are used to capture the conceptual structure of a domain, Visualizations of Ontologies as the third relevant area is critical to establish a common ground for knowledge sharing, this is primarily built on a survey of ontology visualization methods by Katifori et al..
- Data source: two genetics researchers
- Size: 20-30 patients per researcher
- Spatial dimensionality: 2D
- Temporal dimensionality: static
- Type: multivariate
- Sunburst chart
- Matrix plot
- Electronic Health Record Visualization
- Medical focus: Human Phenotype Ontology - Phenotypes
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