Visual Analytics
Visual Analytics - Related Videos
10:58
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Cited by 12 •
2011
Visual analytics (VA) is a new approach of analyzing data interactively. In this video, we discuss the data overload problem brought on by high-throughput biological experiments, and propose VA as a solution to such problem. The video demonstrates analysis within and between immunological datasets using a VA tool called Tableau.
05:40
Fabricating Cotton Analytical Devices
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2016
To investigate simple fabrication approaches for multiple assay needs, we created a fluid-absorbing channel system made of cotton material. This device was used to establish a multiple detection platform, and solve contamination issues that commonly affect lateral flow-based biomedical devices, for clinical urinalysis of nitrite, total protein, and urobilinogen.
09:51
Sample Preparation for Analytical Characterization
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2023
Source: Laboratory of Dr. B. Jill Venton - University of Virginia Sample preparation is the way in which a sample is treated to prepare for analysis. Careful sample preparation is critical in analytical chemistry to accurately generate either a standard or unknown sample for a chemical measurement. Errors in analytical chemistry methods are categorized as random or systematic. Random errors are errors due to change and are often due to noise in instrument. Systematic errors are due to...
02:02
Detection of a Target Analyte Using a Bacterial Biosensor
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2025
Source: Barber, A. E., et al. Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue. J. Vis. Exp. (2019).This video demonstrates the detection of specific analytes using a bacterial biosensor. A bacterial culture carrying a plasmid with a red fluorescent protein (RFP) gene under an analyte-sensitive promoter is taken. After thoroughly wiping the test surface, a small section of the wipe is immersed in the culture and incubated. The analyte...
05:13
Biolayer Interferometry Technology to Detect Interactions between Ligand and Analyte
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2025
In this video, we demonstrate biolayer interferometry (BLI) technology to detect the intermolecular interactions between ligands and target analytes in real-time. BLI measures changes in the interference pattern of white light reflected off a layer of immobilized ligands on a biosensor surface as they interact with analytes in solution.
FAQs
Interactive exploration lets users examine structured datasets through graphical representations while changing the view or focus of analysis. This user-guided process can reveal relationships, trends, and unusual observations that may be difficult to recognize in raw data. As a result, researchers can investigate patterns more directly and develop evidence-based conclusions from complex measurements.
Filtering narrows attention to selected portions of a dataset, while pattern detection helps identify recurring relationships, trends, or outliers within the displayed information. Together, these functions make large or complex datasets easier to examine systematically. They support comparison and interpretation by helping users distinguish potentially meaningful signals from the broader set of observations.
Visual Analytics can bring together measurements of immune cells, cytokines, pathogens, and clinical outcomes within a shared analytical view. This integration helps researchers examine how different types of observations relate to one another rather than interpreting each measurement in isolation. Such comparisons can support clearer assessment of immune responses, disease-related patterns, and treatment effects.
A practical workflow begins by organizing relevant measurements into structured datasets, then representing them graphically for exploration. Researchers can apply filters, inspect detected patterns and outliers, and compare immune or infection-related observations with clinical outcomes. The resulting views support interpretation, hypothesis generation, and communication of findings in laboratory or clinical research.
It is useful when researchers need to compare immune responses, follow disease progression, or evaluate treatment effects across measurements involving immune cells, cytokines, pathogens, and clinical outcomes. By presenting these observations in an interactive form, the approach supports investigation of complex relationships and helps teams examine evidence relevant to both laboratory studies and clinical research.
Visual Analytics can support hypothesis generation, clearer communication of results, and more informed decisions in immunology and infection research. Its displays help researchers examine relationships and trends across biological and clinical information, while interactive analysis allows evidence to be explored from different perspectives. These outcomes can improve how findings are interpreted and discussed.