FLOWJO DEPARTMENTAL LICENSE SOFTWARESimilarly to FlowJo 3, Cytobank 16, OpenCyto 17, and Webflow 18, Freecyto supports machine learning applications, but it does not require the installation of specific software packages (often OS-dependent), a detailed understanding of the software workflow, or extra layers of complexity in displaying, interacting, and sharing the FCM analysis with other researchers. Importantly, while simplifying the data analysis and having the intuitive work flow, Freecyto preserves the key features of traditional FCM software, such as scatterplots (dotplots) of two different emission, histograms of a fluorescent emission measurement 14, the side-by-side comparison of the results between the control and experimental populations and gating on sub-populations of cells. In fact, analysis of flow cytometry experiments, despite having tens of thousands of data points, can be performed and visualized on a mobile device. In this work we developed a new FCM software that facilitates the FCM data analysis, while maintaining the accuracy and resolution of the data. Inherent in this requirement, the datasets that are produced with the conventional FCM software (FlowJo 3, Cytobank 16, OpenCyto 17, and Webflow 18) are typically quite large, which complicates their interactive web analyses. FCM analysis, thus, becomes a computational and statistical challenge that produces meaningful data only if the analysis is adequate for the experimental complexity. 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. 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. the performance of the FCM software that provides quantitative outputs for large numbers of events 2. Successful FCM experiments rely on the accuracy and resolution of the data analysis, e.g. The changes between control and experimental cohorts are often determined through fluorescently tagged antibodies that are specific for given proteins and the fluorescence is examined by microscopy and/or high throughput screening using a flow cytometer 1, 14. A common experimental setup in biomedicine relies on being able to identify specific changes between a control and an experimental cell population. 8– 11, testing therapeutic efficacy of a treatment 12, and, more recently, gene-editing detection workflows 13. Finally, we demonstrate that the data accuracy is preserved when Freecyto is compared to conventional FCM software.įlow cytometry is broadly used in biomedicine, which is exemplified by identification of protein marker expressions 1– 6, determinations of cell-fate and cell cycle progression 7, analysis of pathology-caused changes, e.g. We also show that Freecyto can be applied to the analysis of various experimental setups that frequently require the use of FCM. 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 2. 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. A key limitation of web browsers is their inability to interactively display large amounts of data. FLOWJO DEPARTMENTAL LICENSE FREEFreecyto 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. Here we report a more effective way to analyze FCM data on the web. Current FCM data analysis platforms (FlowJo 3, etc.), while very useful, do not allow interactive data processing online due to the data size limitations. A typical FCM experiment can produce a large array of data making the analysis computationally intensive 2. 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.
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