Flow cytometry is a very powerful tool useful within the quantitative assessment and characterization of large populations of data points. These data points may represent rare needle-in-the-haystack events ranging from cancer cells circulating in the human bloodstream to defective components built from nanoparticles and even the development of biologics within the pharmaceutical industry. The techniques underpinning the analysis of flow cytometry data are useful in revealing insights from populations ranging in the millions. However, they rely on manual gating of clustered events; an approach subject to human error as well as subjective decision making.
Within the last several years, there has been significant development, within academic circles, of automated techniques for gating of clusters derived from flow cytometry data. Such automated approaches have been vetted through peer-review, published, and also packaged as software libraries for use within popular analytics platforms, such as R and Python, rendering their functionality quite accessible from a practical standpoint.
This recent availability of automated gating algorithms has the potential to catalyze significant developments throughout a number of fields, ranging from biofuels, drug discovery, and even cancer detection. The application of high-throughput automated analyses of data from flow cytometry assessments may reduce the time necessary to exclude dead-end experiments, enhance lead generation, and advance the field of personalized cancer research as well as companion diagnostics, which are dependent on flow cytometry.
OpenCyto, a framework available for use within the R platform, is a collection of open-source packages from the BioConductor suite serving as infrastructure for flow cytometry data analysis. The collection of packages includes tools for the import of classic data files, visualization of cytometry data, as well as tools used in gating based on published statistical methods. The OpenCyto framework may be used, in conjunction with a consultant well-versed in automation and data capture, in creating and delivering valuable high-throughput automated analysis workflows. A small subset of packages within OpenCyto provide access necessary to a wide variety of automated gating methods, in addition to providing flexibility for end-users to integrate their own custom-built gating protocols.
Many industries currently leverage flow cytometry to perform quantitative assessments within their respective applications. OpenCyto in combination with recently published automated gating methods could help in unleashing significant value from processes currently being performed manually. Consultants knowledgeable in the fields of automation, flow cytometry, and data science would serve towards implementing and enhancing the value proposition of a high-throughput automated approach towards the analysis of data from flow cytometry.