Flow cytometers guide fluorescently labeled cells one by one past a series of lasers and detectors in order to record their physical and molecular characteristics. Researchers using these techniques can survey tens or even hundreds of thousands of cells, garnering information that allows them not only to enumerate known cell types (such as CD4+ and CD8+ T cells) but also to identify novel subpopulations they may never have known were there.
But data collection is only the first part of the story; in flow cytometry it’s the analysis that counts. At a minimum, flow cytometry software packages must be able to load raw data files, transform the recorded intensity values onto a logarithmic scale, “gate” the information (i.e., identify threshold values to define whether a cell expresses a given marker), and plot the results.
Flow cytometers include their own software packages, of course, and third-party commercial analysis tools also exist. But these packages are typically relatively slow to adopt advances coming out of academic labs, says Ryan Brinkman, a Distinguished Scientist in the Terry Fox Laboratory at the BC Cancer Agency in Vancouver. As a result, a healthy collection of free and open-source alternatives has sprung up over the past decade or so. Brinkman even co-organizes periodic open competitions called FlowCAP to test them (flowcap.flowsite.org).