Data visualization refers to the graphical representation of data and serves as a  bridge between raw data and actionable insights. It allows  researchers to identify trends, correlations, and patterns within large datasets. Researchers can make informed decisions quickly by turning raw data into visual representations. These visual tools not only enhance understanding but also facilitate collaboration among stakeholders. This  is crucial in biomedical research  as it  supports interpretation of complex biological and clinical data.

Innovations in data visualization hold the potential to unlock new discoveries and improve patient outcomes. The rise of  big data and its complexities can’t be suitably navigated using traditional methods as they often fall short in garnering comprehensive insights.The recent advancements  in data visualization—such as interactive dashboards, AI-enhanced tools, and real-time analytics have transformed the field of research. It enables researchers to engage more deeply with  data, and paves the way for collaborative research and data-driven discoveries.

This blog takes you through the recent advancements, challenges, and future directions in visualization of biomedical data.

The Evolution of Data Visualization in Biomedical Research

"Data visualization is an aid from the beginning to the end of the typical data science pipeline. It improves the understanding and communication of the data for both data experts and end users."
— Samara Vazquez Perez, Data Scientist

In the 1860s, Gregor Mendel, often referred to as the father of genetics, used the Punnett square to record and interpret his data. This visualization technique became a powerful tool for understanding how traits are inherited. Even before Mendel’s work, various forms of data visualization played a crucial role in helping scientists make sense of complex data and observations.

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Today, life science data has become  machine-readable but is less accessible to human interpretation. With the advent of high-throughput technologies and bioinformatics, researchers are equipped with a repertoire of  tools capable of generating, visualizing and analyzing vast volumes of molecular data. Over time, visualization tools have evolved to accommodate more complex data types, such as multi-omics data (e.g., genomics, proteomics, and metabolomics) and 3D models of protein structures, deepening our understanding of biological mechanisms. Modern visualization tools now need to manage diverse datasets—from transcriptomics to patient-derived data—in order to enable researchers to interpret complex information without becoming overwhelmed.

Current Innovations in Data Visualization

Interactive Visualizations

One of the most significant advancements in data visualization is interactivity. Interactive tools like Spotfire, Tableau and Cellxgene allow researchers to explore datasets dynamically. This enables them to filter, adjust, and delve into specific data points as needed. It further makes data analysis more engaging, facilitates quicker hypothesis testing, and enables customized data views.

Machine Learning and AI Integration

The integration of machine learning (ML) with visualization tools has transformed the way researchers analyze complex datasets. ML models can identify hidden trends, clusters, and provide predictive insights, which are then visualized to support better decision-making. Elucidata also employs ML to  harmonize data to facilitate accurate data visualization, which enables qualitative research.

3D Visualization

3D visualization plays a critical role in understanding complex biological structures, such as protein-ligand interactions, cellular architectures, and anatomical data. These visualizations facilitate better comprehension of spatial relationships for researchers, as contrasted with use of 2D models alone.

Tools like PyMOL and Chimera have long been used in molecular biology for this purpose, but new platforms are emerging with enhanced interactivity and integration capabilities.

Real-Time Data Visualization

Real- time data visualization is crucial for making time-sensitive decisions in clinical research and diagnostics. Real-time dashboards and visualizations such as Power BI, Grafana and Looker Studio enable researchers to monitor live data streams, such as patient responses during clinical trials or real-time monitoring of molecular assays. This is a game-changer in scenarios where speed is critical including pandemic response efforts, where tracking the spread of disease and evaluating treatment efficacy requires up-to-the-minute insights.

Challenges in Biomedical Data Visualization

Despite recent advancements, biomedical data visualization still faces notable challenges like management of large datasets, the integration of multi-omics data, and meeting the demand for rapid insights:

Data Volume, Quality and Interoperability

Data overload is a common issue, wherein researchers struggle to distill insights from overwhelming data volumes. Ensuring data quality and consistency is another challenge, as poor-quality data can lead to misleading visualizations. Additionally, making these advanced visualizations accessible to a non-expert audience remains a hurdle, as clarity is crucial for cross-disciplinary collaboration. Data Interoperability remains another significant challenge, specifically when data needs to be integrated from different sources or systems, as it necessitates standardization and harmonization of data.

Diverse solutions: One size DOESN'T fit all

The landscape of data visualization in biomedical research is rich with diverse options. Popular open-source tools like R’s ggplot2 and Python’s Seaborn offer flexibility, although they demand programming skills. In contrast, commercial platforms like Power BI and Tableau provide user-friendly interfaces but may lack customization required for niche research needs. The effective use of these tools depends greatly on the skill and experience required to select the appropriate platform or pipeline, which ensures  that visualizations are both meaningful and accurate.

While certain areas require highly specialized visualization tools, there are also common visualization challenges that can be tackled with general methods and strategies. As poor visualization practices  persist,  there are also promising developments that could improve how we interpret and engage with data.

Elucidata's Innovative Solutions

Elucidata offers comprehensive services and solutions for biomedical data visualization. Raw biomedical data, whether public or proprietary, is harmonized and organized within atlases that  creates  a unified data resource for the user. This data can be analyzed through custom applications or visualized using third-party tools and interactive dashboards based on user needs. The foundation of Elucidata’s approach lies in delivering high-quality, harmonized, and ML-ready data, and offering  expertise in developing custom visualization solutions tailored to specific requirements.
Eager to know more? Check out the services and solutions below:

Custom Shiny Applications

Elucidata supports the development of custom biomedical data visualization applications. It allows researchers to build tailored dashboards,  which facilitates  a deeper exploration of molecular data. Its AI capabilities enhance data visualization, making it easier to derive insights from large, and  multi-dimensional datasets. Elucidata helps researchers translate data into meaningful insights  by providing visualization solutions that are both user-friendly and customizable.

For example, a client’s prototype tool—an R Shiny-based application designed for visualizing single-cell datasets—was significantly enhanced by Elucidata’s engineering team.  The team enabled seamless integration with the client’s proprietary libraries for single-cell analysis by optimizing the tool and redeploying it as a Python-based application. This transformation allowed users to perform advanced statistical operations, such as correlation analysis and differential gene expression, directly through the graphical user interface.

Read our case study here.

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Customized Dashboards

Elucidata offers customizable dashboards that allow users to explore their data without needing extensive coding knowledge. Leading platforms such as Tableau, Spotfire and D3.js have set the standard for interactive data visualization across industries, but they are often found lacking  when it comes to handling the specificity of biomedical data. Elucidata fills this gap by offering features designed specifically for life sciences, such as custom integrations with omics data repositories and sample-level metadata handling.

We developed a custom visualization dashboard that seamlessly integrates with our data model for one of our customers. This dashboard empowered the researchers to navigate data effortlessly and uncover meaningful insights with just a single click.  Researchers could intuitively explore cytokine variations over time through a user-friendly interface by leveraging the custom dashboard and the harmonized data on our platform.

Read the case study here.

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A brief look at a custom dashboard developed to track patient response to cytokine IL7.

Seamless Integration of Third-party Applications

Elucidata  also optimizes the deployment of third-party applications like Cellxgene and Phantasus on Polly using docker technology, which enhances  researchers' access to sophisticated tools for data exploration. By leveraging machine learning (ML) algorithms for data harmonization, we ensure that researchers interact with high-quality, standardized data. This improves the accuracy and relevance of visualizations as well as analyses performed on these third-party applications. This streamlined integration enables users to explore multi-omics data seamlessly within the same environment, where clean, harmonized data directly supports robust visualizations and insights. Elucidata’s platform is also flexible, as it facilitates   rapid inclusion of additional applications based on user needs, and creates  an adaptable ecosystem which  evolves with scientific advancements and research demands.

Visualization of cell types by Cellxgene on Polly

Real World Impact of Efficient Data Visualization

Data visualization doesn’t just make research easier—it drives better outcomes.

Data visualization doesn’t just make research easier—it drives better outcomes.
Data visualization has been pivotal in recent research efforts, including COVID-19 studies, where interactive visualizations tracked viral mutations and their geographical spread. Projects like Nextstrain and Global Dashboards leveraged genomic data, mapping out regional mutations and providing a visual representation of transmission trends essential for public health responses. Interactive data visualization tools have also made it possible to explore gene interactions in the Cancer Genome Atlas project, enhancing our understanding of oncogenes and tumor suppressors. In oncology research, visualization tools enable the identification of novel drug targets by mapping out complex interactions between genes and pathways. Tools such as Cytoscape have also been applied in cancer research to study signaling pathways and predict interactions, thus guiding precision medicine approaches.

Elucidata, has contributed to several success stories featuring the crucial role of data visualization. . From analyzing clinical assay data for translational research to validating probiotic treatments for malnourished children, and deriving insights from large volumes of drug screening data, our visual tools have unlocked valuable discoveries.

Head to our case studies section to read more.

With advancements in AI, visualization tools will increasingly move beyond interpretation to actively suggest potential research directions through predictive modeling. Elucidata continues to be at the forefront of these innovations, integrating advanced analytics with intuitive visualization to redefine the possibilities in data analysis.

Connect with us to take your biomedical data visualization journey to the next level!

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