Authors (@slack): Omar Holayell (Holayell)
Data visualisations of cancer
Data visualisations of cancer is the use of visual representations to understand, analyse, and communicate data related to cancer research and treatment andOne of the advantages of using R is that researchers have a much larger range of fully customizable data visualisations options than are typically available in point-and-click software because of the open-source nature of R. These visualisation options not only look attractive but also can increase transparency about the distribution of the underlying data rather than relying on commonly used visualisations of aggregations(1)
Bar Chart, Gantt Chart, Heat Map, Histogram Diagram, Network Diagram, Pie Chart, Scatter Plot (2)
1- Enhancing Understanding of Complex Data : Cancer research often involves large and complex datasets, including genomic, proteomic, and clinical data. Visualisation techniques such as heatmaps, scatter plots, and 3D models allow researchers to identify patterns, trends, and anomalies that may not be apparent in raw data. For instance, the use of dimensionality reduction techniques like UMAP (Uniform Manifold Approximation and Projection) can help visualise high-dimensional single-cell RNA sequencing data(3)
2- Facilitating Communication of Findings : Effective communication of research findings is critical for collaboration among scientists, clinicians, and stakeholders. Visualisations can convey complex information quickly and clearly, making it easier to share insights with non-experts. For example, the use of interactive visual tools can help pathologists and oncologists better understand tumour microenvironments and the implications for treatment
3- Aiding in Decision-Making for Treatment Strategies Data visualisation tools can assist clinicians in making informed decisions regarding treatment options. For instance, visualising patient data alongside treatment outcomes can help identify which therapies are most effective for specific cancer types or patient demographics. This is particularly important in personalised medicine, where treatment plans are tailored to individual patient profiles
4- Visualizing Treatment Outcomes and Patient Responses Visualisations can also be used to track treatment outcomes and patient responses over time, providing valuable insights into the effectiveness of therapies. For example, visualising survival curves or response rates can help identify which treatments yield the best outcomes for specific patient populations (4)
data visualisation is a critical component of cancer research that enhances understanding, facilitates communication, aids decision-making, supports collaboration, and visualises treatment outcomes. As the field continues to evolve, the integration of advanced visualisation techniques will be essential for translating complex data into actionable insights that can improve patient care and outcomes.
- Nordmann E, McAleer P, Toivo W, Paterson H, DeBruine LM. Data Visualization Using R for Researchers Who Do Not Use R. Advances in Methods and Practices in Psychological Science. 2022;5(2). doi:10.1177/25152459221074654
- https://www.geeksforgeeks.org/r-charts-and-graphs
- IOBR2: Multidimensional Decoding Tumor Microenvironment for Immuno-Oncology Research Dongqiang Zeng, Yiran Fang, Peng Luo, Wenjun Qiu, Shixiang Wang, Rongfang Shen, Wenchao Gu, Xiatong Huang, Qianqian Mao, Yonghong Lai, Xi Xu, Min Shi, Guangchuang Yu, Wangjun Liao bioRxiv 2024.01.13.575484; doi: https://doi.org/10.1101/2024.01.13.575484
- Digital solutions supporting the quality of life of European cancer patients and their caregivers: a systematic literature review Camilla Ancona, Emanuele Caroppo, Pietro De Lellis medRxiv 2024.06.18.24309065; doi: https://doi.org/10.1101/2024.06.18.24309065