  Flow cytometry (FCM) is an analytic technique that is capable of detecting
and recording the emission of fluorescence and light scattering of cells or
particles (that are collectively called "events") in a population. A typical
FCM experiment can produce a large array of data making the analysis
computationally intensive. Current FCM data analysis platforms (FlowJo, etc.),
while very useful, do not allow interactive data processing online due to the
data size limitations. Here we report a more effective way to analyze FCM data.
Freecyto is a free, easy-to-learn, Python-flask-based web application that uses
a weighted k-means clustering algorithm to facilitate the interactive analysis
of flow cytometry data. A key limitation of web browsers is their inability to
interactively display large amounts of data. Freecyto addresses this bottleneck
through the use of the k-means algorithm to quantize the data, allowing the
user to access a representative set of data points for interactive
visualization of complex datasets. Moreover, Freecyto enables the interactive
analyses of large complex datasets while preserving the standard FCM
visualization features, such as the generation of scatterplots (dotplots),
histograms, heatmaps, boxplots, as well as a SQL-based sub-population gating
feature. We also show that Freecyto can be applied to the analysis of various
experimental setups that frequently require the use of FCM. Finally, we
demonstrate that the data accuracy is preserved when Freecyto is compared to
conventional FCM software.
