14 papers · ranked by Valyu relevance
Anne Thomas Homescu, Teresa Murray
We describe an interactive visualizer (implemented in R Shiny framework) to facilitate analysis and a better understanding of neurotransmitter data collected within the context of epileptic seizures. Given the very high granularity of collected data (at millisecond level), it is challenging to use static visuals and/or…
Muhammed Khawatmi, Heba Sailem
Visualization of spatial datasets is essential for understanding biological systems that are composed of several interacting cell types. For example, gene expression data at the molecular level needs to be interpreted based on cell type, spatial context, tissue type, and interactions with the surrounding environment.…
Jordan K Matelsky, Joseph Downs, Hannah Cowley, Brock Wester + 1 more
As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborators and with the public can be especially onerous, as it is challenging to reconcile software dependencies, data…
Muhammad Zain Butt, Rana Sheraz Ahmad, Eman Fatima, Muhammad Tahir ul Qamar
The application of Large Language Models (LLMs) for generating data visualizations through natural language interaction represents a promising advance in AI-assisted scientific analysis. However, existing LLM-based tools largely emphasize graph generation, while research workflows require not only visualization but…
Svetlana Ovchinnikova, Simon Anders
In any research project involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. In data quality assessment, for example, it might be very helpful to have one…
Zhigang Lu, Yanyan Zhang
In biological research analysis of large data sets, such as RNA-seq gene expression, often involves visualisation of thousands of data points and associated database query. Static charts produced by traditional tools lack the ability to reveal underlying information, and separated database query is laborious and…
Arsenij Ustjanzew, Jens Preussner, Mette Bentsen, Carsten Kuenne + 1 more
Data visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data. A suitable visualization has to be intuitive and accessible. Web-based dashboards have become popular tools for the arrangement, consolidation and display of such…
Anton Stratmann, Martin Beyß, Johann F. Jadebeck, Katharina Nöh
Understanding input-output relationships within multivariate datasets is an ubiquitous task in the life and data sciences. For this, visual analysis is indispensable for providing expressive summaries and preparing decision-making. We present the visual analysis approach and software MooViE, which is designed to strike…
Yimin Zheng, Zhihang Zheng, André F. Rendeiro, Edwin Cheung
Contemporary data visualization is challenged by the growing complexity and size of datasets, often comprising numerous interrelated features. Traditional visualization methods struggle to capture these complex relationships fully or are specialized to a domain requiring familiarity with multiple visualization tools.…
Zrinko Kozic, Sam Booker, Owen Dando, Giles Hardingham + 1 more
A key step in understanding the results of biological experiments is visualization of the data. Many laboratory experiments contain a range of measurements that exist within a hierarchy of interdependence. An automated way to visualise and interrogate experimental data would: 1) lead to improved understanding of the…
Boyi Guo, Stephanie C. Hicks
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve…
Simon Warchol, Robert Krueger, Ajit Johnson Nirmal, Giorgio Gaglia + 9 more
New multiplexed tissue imaging technologies have enabled the study of normal and diseased tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis as well as following treatment. Yet…
Muhammed Khawatmi, Yoann Steux, Sadam Zourob, Heba Sailem
Intuitive visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns in complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we…
Simon Warchol, Grace Guo, Johannes Knittel, Dan Freeman + 4 more
Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial…