AutoGate: automating analysis of flow cytometry data


Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots). Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. Ultimately, this process generates analysis “trees” that can be applied to automatically guide analyses for similar samples. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. When this “early adopter” phase is complete, the authors expect to distribute the software free of charge to .edu, .org and .gov users.

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    This software in its entirety is appropriately designated “semi-automated” since the user remains in control and is required to “supervise” the process by making choices and providing other input at key points during the process. Once the user provides this input, the compensation, clustering and other statistical operations are triggered to perform as automated tasks that return to the user for inspection of output and for input of further guidance. We use the term “automated” here for textual simplicity, not to imply full automation of the analysis process, which (in our view) would not be an acceptable in a tool intended for the complex processes involved in flow data analysis.


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Work described in this article is sponsored in part by the NIH Grant number R01AI098519.

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Correspondence to Leonore A. Herzenberg.

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Meehan, S., Walther, G., Moore, W. et al. AutoGate: automating analysis of flow cytometry data. Immunol Res 58, 218–223 (2014).

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  • Multiparameter flow cytometry
  • Automating fluorescence compensation
  • Automatic cell subsets identification
  • Guiding gating strategy