Application

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Currently, 17 studies with 570 humans and non-human primates have 84 dimensions to explore, and more studies and assays are in the pipeline. Treatments include placebos, active controls, “challenges” to animals in which they are exposed to an infectious HIV-like virus, and 35 vaccines and adjuvants on varied schedules of administration. Four assays provide measures of immune system response to these treatments based on testing subject samples against antigens: the binding antibody multiplex assay (4 antibody isotypes and 19 antigens), ELISpot (IFNγ for 14 antigens), intracellular cytokine staining (2-cytokine response for 2 cell types against 13 antigens), and neutralizing antibody (3 target cells and 89 antigens). In some cases, we combined results from multiple labs running the same standardized assay. During prototyping, we demonstrated scalability to hundreds of real studies with thousands of subjects and we plan to expand to that scale again.
The DataSpace homepage has usage tips, news, and access to previously saved groups and plots. The Learn section is a unique source of extensive metadata for hundreds of studies, vaccine products, and assays, hyperlinked by association. Find Subjects is for filtering cohorts by categorical values, Plot supports up to 3 variables, and the Data Grid shows a spreadsheet view for filter or export. A session begins with all data available. Filters made in one place affect every other data view. We describe select visual analytics capabilities in more detail below.

Info Pane

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A persistent pane on the right lists active filters and lets investigators track the key data attributes that the filters affect. Without this list, it is easy to change a plot or filter without realizing ancillary effects like removing studies or skewing the subject population. At times, these counts either might be candidate explanations of a visual pattern or confounds to another explanation. Clicking a count shows the current filtered values and allows direct change. We worked with investigators to prioritize the most important counts, including subjects, species, studies, products, treatments, and time points (Figure 2).

Find Subjects

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This section has a bar chart counting the number of subjects in the DataSpace with different attributes, such as their species or the vaccines they received. Like the cohort selection from Bernard et al., brushing shows overlap in other values, enabling, for example, seeing how many subjects were additionally given any other product (such as a complementary vaccine or an adjuvant) (Figure 1). Filtering with a bar value removes all the subjects without the attribute and keeps the subjects that have overlapping values. Filters affect all other data views in the application. Controls at the top for changing the attribute and its hierarchical sorting allow discovery of attribute relationships in available data and serial filtering such as, ’all macaques that received any kind of DNA vaccine that were tested on the neutralizing antibody assay.’ Unlike Bernard et al., we