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Presenter: Ryan, Brinkman, Vancouver, BC, Canada
Authors: Ryan Brinkman
Learning Objectives:
1. Awareness of the overall availability and utility of free/open-source high throughput flow cytometry analysis tools.
2. Specific appreciation of the pitfalls, benefits and relative performance of automated gating approaches.
3. Familiarity of how automated flow cytometry data analysis can be applied, illustrated through the use of practical examples.
Traditionally, the majority of FCM experiments have been analyzed manually, with most effort devoted to visually identifying cell populations of interest by serial inspection of one or two-dimensional projections of up to 16 dimensional data. However, the amount of data generated by high throughput, high-dimensional flow cytometry poses unique informatics and statistical challenges that makes this approach a bottleneck to discovery. While most of the currently available analysis tools are designed to facilitate this manual gating, this process necessarily neglects the higher dimensionality of the data, is time consuming, subjective and difficult to accurately reproduce. To address these shortcomings, free/open-source automated approaches have been developed to facilitate data mining of flow cytometry datasets. The utility and relative performance of these tools will be explored and illustrated with examples of their application to discovery and diagnosis.
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