Freecyto: Quantized Flow Cytometry Analysis for the Web

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 an...

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Bibliographic Details
Published inarXiv.org
Main Authors Wong, Nathan, Kim, Daehwan, Robinson, Zachery, Huang, Connie, Conboy, Irina M
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.11.2020
Subjects
Algorithms
Applications programs
Cluster analysis
Clustering
Data analysis
Data points
Data processing
Datasets
Emission analysis
Flow cytometry
Fluorescence
Histograms
Light scattering
Query languages
Vector quantization
Visualization
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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. 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.
ISSN:2331-8422