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Updated: May 28, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE.

Peng Qiu1, Erin F Simonds, Sean C Bendall

  • 1Department of Radiology, Stanford University, Stanford, CA, USA. pqiu@mdanderson.org

Nature Biotechnology
|October 4, 2011
PubMed
Summary

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We developed Spanning-tree Progression Analysis of Density-Normalized Events (SPADE), a computational method to objectively analyze cellular heterogeneity. SPADE organizes cells into related phenotypes, aiding in cell type identification and analysis of functional markers.

Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Analyzing cellular heterogeneity is crucial for understanding biological systems.
  • Current methods for high-dimensional single-cell data analysis are often subjective and labor-intensive.
  • Objective computational approaches are needed to uncover cellular heterogeneity.

Purpose of the Study:

  • To present a versatile computational approach, SPADE, for objective analysis of cellular heterogeneity.
  • To demonstrate SPADE's ability to organize cells into a hierarchy of related phenotypes.
  • To validate SPADE's robustness and utility in analyzing single-cell data.

Main Methods:

  • Developed Spanning-tree Progression Analysis of Density-Normalized Events (SPADE).
  • Applied SPADE to flow cytometry data of mouse bone marrow.

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Last Updated: May 28, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Sample Preparation for Mass Cytometry Analysis
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  • Applied SPADE to mass cytometry data of human bone marrow.

    • Main Results:

    • SPADE organized cells into a hierarchy of related phenotypes in both mouse and human bone marrow data.
    • The identified phenotypes partially recapitulated known patterns of hematopoiesis.
    • SPADE demonstrated robustness to measurement noise and choice of cellular markers.

    Conclusions:

    • SPADE provides an objective and versatile computational approach for analyzing cellular heterogeneity.
    • SPADE facilitates cell type identification and comparison of functional markers.
    • SPADE is a valuable tool for studying cellular heterogeneity in biological systems and in response to perturbations.