Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Sep 15;28(18):2400-1.
doi: 10.1093/bioinformatics/bts425. Epub 2012 Jul 10.

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data

Michael D Linderman  1 , Zach Bjornson, Erin F Simonds, Peng Qiu, Robert V Bruggner, Ketaki Sheode, Teresa H Meng, Sylvia K Plevritis, Garry P Nolan
Affiliations

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data

Michael D Linderman et al. Bioinformatics. .

Abstract

Motivation: Recent advances in flow cytometry enable simultaneous single-cell measurement of 30+ surface and intracellular proteins. CytoSPADE is a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events algorithm for tree-based analysis and visualization of this high-dimensional cytometry data.

Availability: Source code and binaries are freely available at http://cytospade.org and via Bioconductor version 2.10 onwards for Linux, OSX and Windows. CytoSPADE is implemented in R, C++ and Java.

Contact: michael.linderman@mssm.edu

Supplementary information: Additional documentation available at http://cytospade.org.

PubMed Disclaimer

Figures

Fig. 1.
Fig. 1.
Structure (a) of CytoSPADE, including the R-package and the user interface (b) implemented as a Cytoscape plugin. Using the Cytoscape plugin, users can simultaneously view the SPADE tree (right panel) and the underlying cytometry data (biaxial plot in left panel). The R package can be used independently of the Cytoscape plugin. Bar charts (c) show the performance of the multi-threaded R package on a dual-socket Intel Xeon 2.27 GHz server with 12 GB of RAM for a variety of cytometry datasets and clustering parameters, including high-dimensional mass cytometry datasets with millions of cells.
See this image and copyright information in PMC

References

    1. Bendall SC, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332:687–696. - PMC - PubMed
    1. Cline MS, et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2007;2:2366–2382. - PMC - PubMed
    1. Hahne F, et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics. 2009;10:106. - PMC - PubMed
    1. Kotecha N, et al. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 2010 Chapter 10, Unit10.17. - PMC - PubMed
    1. Lo K, et al. flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics. 2009;10:145. - PMC - PubMed

Publication types