Abstract
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 population1. A typical FCM experiment can produce a large array of data making the analysis computationally intensive2. Current FCM data analysis platforms (FlowJo3, 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 on the web. Freecyto is a free and intuitive 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 feature2. 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.
Highlights
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 in a population[1]
Abbreviations FCM Flow cytometry Event(s) Emission(s) of fluorescence and light scattering of cells or particles t-SNE Barnes-Hut approximation of t-distributed stochastic neighbour embedding K-means Lloyd’s Algorithm with Euclidean distances for k-means clustering (k-means++ is used for cluster center initialization)
Flow cytometry is broadly used in biomedicine, which is exemplified by identification of protein marker expressions[1,2,3,4,5,6], determinations of cell-fate and cell cycle p rogression[7], analysis of pathology-caused changes, e.g. cancer promoted, immune-skewing, etc.[8,9,10,11], testing therapeutic efficacy of a treatment[12], and, more recently, gene-editing detection w orkflows[13]
Summary
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[1]. In FCM analysis, an event is constituted by the cytometer’s detection of fluorescence emission and/or light scatter signals from a single cell or particle that passes through the microfluidic flow chamber. With thousands of these events, individual measures of fluorescence, size and granularity are produced, and to add complexity, these measurements can be deliberately modified by a researcher through the instrument setup, which can be changed from run to run[15]. Analysis is adequate for the experimental complexity Inherent in this requirement, the datasets that are produced with the conventional FCM software ( FlowJo3, Cytobank16, OpenCyto[17], and Webflow18) are typically quite large, which complicates their interactive web analyses
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