Data Sciences Institute, University of Toronto, Toronto, ON M5G 1Z5, Canada.
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada.
Gigascience. 2024 Jan 2;13. doi: 10.1093/gigascience/giae056.
As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users.
We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells.
TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.
随着单细胞测序技术的不断进步，随之而来的大量且复杂的数据带来了新的分析挑战。单细胞图谱中的大型细胞群体更难以可视化，并且需要进行大量处理才能识别出具有生物学意义的亚群。管理这些工作流程对于技术用户来说很繁琐，对于非技术用户来说也不直观。
我们提出了 TooManyCellsInteractive（TMCI），这是一个基于浏览器的 JavaScript 应用程序，用于交互式探索细胞群体。TMCI 提供了一个直观的界面来可视化和操作层次细胞亚群的径向树表示形式，并允许用户轻松地在多个分辨率上叠加、过滤和比较生物学特征。在这里，我们描述了软件架构，并展示了如何在泛癌分析中使用 TMCI 来识别药物耐受持久细胞中的独特生存途径。
TMCI 将以用户友好的方式促进大规模测序数据的探索和可视化。TMCI 可在 https://github.com/schwartzlab-methods/too-many-cells-interactive 上免费获得。本文内的数据的示例树可在 https://tmci.schwartzlab.ca/ 上获得。
