While radial tree visualizations offer a unique way to represent hierarchical relationships among cells, they come with several potential limitations. One significant limitation is the difficulty in interpreting complex relationships when the tree becomes too dense or large. As the number of nodes increases, the radial layout can become cluttered, making it challenging for users to discern meaningful patterns or relationships among subpopulations.
To address this limitation, TMCI could implement features such as adaptive zooming and panning capabilities that allow users to focus on specific regions of the tree without losing context. Additionally, incorporating a filtering mechanism that enables users to selectively display only the most relevant nodes based on specific criteria (e.g., expression levels, node size) could help reduce clutter and enhance interpretability.
Another limitation is the potential for misinterpretation of distances between nodes, as radial trees do not always accurately represent the true relationships among data points. TMCI could improve this by integrating distance metrics that provide users with quantitative measures of similarity or dissimilarity between nodes, allowing for a more nuanced understanding of the relationships within the data.
Lastly, TMCI could benefit from incorporating alternative visualization methods alongside the radial tree, such as heatmaps or scatter plots, to provide complementary perspectives on the data. This multi-faceted approach would allow users to cross-reference findings and gain a more comprehensive understanding of the underlying biological processes.