TMCI could be extended to support the visualization and exploration of other types of hierarchical biological data, such as spatial transcriptomics and multi-omics data, by incorporating several key features. First, the application could integrate spatial information by allowing users to visualize the spatial distribution of cells within tissue sections alongside the radial tree structure. This could be achieved by overlaying spatial transcriptomics data onto the existing tree visualization, enabling users to see how cellular hierarchies relate to their physical locations within a tissue.
Additionally, TMCI could be adapted to handle multi-omics data by allowing users to upload and visualize different types of omics data (e.g., transcriptomics, proteomics, metabolomics) simultaneously. This could involve creating a multi-layered visualization where each layer represents a different omic dataset, with the ability to toggle between layers or view them in conjunction. Users could also benefit from advanced filtering options that allow them to explore correlations between different omic layers, enhancing the understanding of complex biological processes.
Furthermore, implementing interactive features such as dynamic clustering algorithms that can adapt to the specific characteristics of spatial or multi-omics data would enhance the usability of TMCI. By providing tools for real-time exploration and manipulation of these datasets, TMCI could facilitate deeper insights into the relationships between cellular states and their spatial or multi-omic contexts.