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Cytobank: Providing an Analytics Platform for Community Cytometry Data Analysis and Collaboration

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Book cover High-Dimensional Single Cell Analysis

Part of the book series: Current Topics in Microbiology and Immunology ((CT MICROBIOLOGY,volume 377))

Abstract

Cytometry is used extensively in clinical and laboratory settings to diagnose and track cell subsets in blood and tissue. High-throughput, single-cell approaches leveraging cytometry are developed and applied in the computational and systems biology communities by researchers, who seek to improve the diagnosis of human diseases, map the structures of cell signaling networks, and identify new cell types. Data analysis and management present a bottleneck in the flow of knowledge from bench to clinic. Multi-parameter flow and mass cytometry enable identification of signaling profiles of patient cell samples. Currently, this process is manual, requiring hours of work to summarize multi-dimensional data and translate these data for input into other analysis programs. In addition, the increase in the number and size of collaborative cytometry studies as well as the computational complexity of analytical tools require the ability to assemble sufficient and appropriately configured computing capacity on demand. There is a critical need for platforms that can be used by both clinical and basic researchers who routinely rely on cytometry. Recent advances provide a unique opportunity to facilitate collaboration and analysis and management of cytometry data. Specifically, advances in cloud computing and virtualization are enabling efficient use of large computing resources for analysis and backup. An example is Cytobank, a platform that allows researchers to annotate, analyze, and share results along with the underlying single-cell data.

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Acknowledgments

The authors would like to thank J. Irish and P. Krutzik for continued discussions and G. Kraker for help with analyses. The Cytobank project has been funded in part by the NIH including NHLBI Contract No. HHSN268201300037C, NIGMS Grant No. GM096579 and NIAID Grant No. AI094929.

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Authors and Affiliations

  1. Cytobank, Inc, Mountain View, CA, USA

    Tiffany J. Chen & Nikesh Kotecha

Authors
  1. Tiffany J. Chen
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  2. Nikesh Kotecha
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Corresponding author

Correspondence to Nikesh Kotecha .

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Editors and Affiliations

  1. Center for Clinical Sciences Research, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, USA

    Harris G. Fienberg

  2. Center for Clinical Sciences Research, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, USA

    Garry P. Nolan

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© 2014 Springer-Verlag Berlin Heidelberg

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Chen, T.J., Kotecha, N. (2014). Cytobank: Providing an Analytics Platform for Community Cytometry Data Analysis and Collaboration. In: Fienberg, H., Nolan, G. (eds) High-Dimensional Single Cell Analysis. Current Topics in Microbiology and Immunology, vol 377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/82_2014_364

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