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This study introduces Collapsible Tree, a JavaScript framework and web app for visualizing hierarchical data, such as single-cell transcriptomics. It integrates cell lineage and gene expression for clearer biological pattern exploration.

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Area of Science:

  • Bioinformatics
  • Data Visualization
  • Computational Biology

Background:

  • Effective data visualization is key for interpreting complex biological datasets, particularly hierarchical structures found in single-cell transcriptomics.
  • Existing methods like t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) often require separate visualizations for cell types and gene expression, potentially obscuring hierarchical relationships.

Purpose of the Study:

  • To develop an integrated visualization tool that combines hierarchical lineage structures with associated data values.
  • To enable intuitive exploration of gene expression patterns within the context of cell development and lineage relationships.

Main Methods:

  • Development of a JavaScript framework for creating interactive, expandable, and collapsible tree structures.
  • Implementation of a web application, Collapsible Tree, to showcase the framework's capabilities.

Main Results:

  • The Collapsible Tree framework successfully integrates lineage structures and gene expression data into a single hierarchical plot.
  • This integrated visualization facilitates the comparison of gene expression across different cell lineages and sub-lineages.
  • The tool reveals complex value patterns that may be missed by conventional methods like UMAP and t-SNE.

Conclusions:

  • Collapsible Tree offers a powerful new approach for visualizing and exploring complex biological data, particularly from single-cell transcriptomics.
  • The framework enhances the interpretability of hierarchical biological structures and associated quantitative data.