IFC soft: visual comparison of flow cytometry data using self-organizing maps

  • Authors:

  • Kyle Thayer;Li Xiong;Vicki Hertzberg

  • Affiliations:

  • Emory University, Atlanta, GA, USA;Emory University, Atlanta, GA, USA;Emory University, Atlanta, GA, USA

  • Venue:
  • Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
  • Year:
  • 2012

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Abstract

Using flow cytometry (FCM) technology, multi-faceted measurements can be taken of many individual particles, and thus is often used in tissue sample analysis. As the resulting data set can have over ten dimensions and millions of points, analysis can be complicated. Visualizing the data requires significantly reducing the number of dimensions or condensing the volume of the data. In studies using FCM data from multiple patients at multiple times, difficulties are compounded. We demonstrate IFC Soft, a program that uses Self-Organizing Maps (SOMs) to visually analyze and compare FCM data. Taking pre-processed FCM data from a transplant study, we show how to use SOMs to select clusters of cells and then to find trends among different classes of patients.