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Updated: May 2, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data.

Conor Klamann1, Christie J Lau2,3, Javier Ruiz-Ramírez2

  • 1Data Sciences Institute, University of Toronto, Toronto, ON M5G 1Z5, Canada.

Gigascience
|August 22, 2024
PubMed
Summary
This summary is machine-generated.

TooManyCellsInteractive (TMCI) is a new tool that simplifies the exploration of large single-cell sequencing datasets. This application offers an intuitive interface for visualizing and analyzing complex cell populations, making data more accessible.

Keywords:
big databrowser-basedcell linedata visualizationdrug-tolerant persister cellshierarchical clusteringinteractive graphical user interfacesingle-cell sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell sequencing generates vast, complex datasets, posing analytical challenges for visualization and subpopulation identification.
  • Managing large-scale single-cell data workflows is difficult for both technical and non-technical users.
  • Existing tools struggle with the scale and complexity of modern single-cell atlases.

Purpose of the Study:

  • To develop an intuitive, browser-based application for interactive exploration of large single-cell sequencing data.
  • To enable easier visualization and manipulation of hierarchical cell subpopulations.
  • To facilitate the identification of biologically relevant cell clusters and features.

Main Methods:

  • Development of TooManyCellsInteractive (TMCI), a JavaScript application.
  • Implementation of a radial tree visualization for hierarchical cell subpopulations.
  • Integration of features for overlaying, filtering, and comparing biological data at multiple resolutions.
  • Application of TMCI to a pan-cancer analysis.

Main Results:

  • TMCI provides an intuitive interface for interactive exploration of cell populations.
  • The application allows visualization and manipulation of hierarchical cell subpopulations.
  • TMCI was successfully used in a pan-cancer analysis to identify survival pathways in drug-tolerant persister cells.
  • Users can easily overlay, filter, and compare biological features across different cell resolutions.

Conclusions:

  • TMCI enhances the exploration and visualization of large-scale sequencing data.
  • The user-friendly interface makes complex data analysis more accessible.
  • TMCI is freely available, promoting wider adoption in biological research.
  • The tool aids in identifying unique survival pathways, as demonstrated in the pan-cancer study.