K-means quantization for a web-based open-source flow cytometry analysis platform

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 an...

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Autores principales:	Nathan Wong, Daehwan Kim, Zachery Robinson, Connie Huang, Irina M. Conboy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a5da9ab9605f4656a4f1bf3e6ea54639
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spelling	oai:doaj.org-article:a5da9ab9605f4656a4f1bf3e6ea546392021-12-02T14:02:54ZK-means quantization for a web-based open-source flow cytometry analysis platform10.1038/s41598-021-86015-62045-2322https://doaj.org/article/a5da9ab9605f4656a4f1bf3e6ea546392021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86015-6https://doaj.org/toc/2045-2322Abstract 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.Nathan WongDaehwan KimZachery RobinsonConnie HuangIrina M. ConboyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nathan Wong Daehwan Kim Zachery Robinson Connie Huang Irina M. Conboy K-means quantization for a web-based open-source flow cytometry analysis platform
description	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.
format	article
author	Nathan Wong Daehwan Kim Zachery Robinson Connie Huang Irina M. Conboy
author_facet	Nathan Wong Daehwan Kim Zachery Robinson Connie Huang Irina M. Conboy
author_sort	Nathan Wong
title	K-means quantization for a web-based open-source flow cytometry analysis platform
title_short	K-means quantization for a web-based open-source flow cytometry analysis platform
title_full	K-means quantization for a web-based open-source flow cytometry analysis platform
title_fullStr	K-means quantization for a web-based open-source flow cytometry analysis platform
title_full_unstemmed	K-means quantization for a web-based open-source flow cytometry analysis platform
title_sort	k-means quantization for a web-based open-source flow cytometry analysis platform
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a5da9ab9605f4656a4f1bf3e6ea54639
work_keys_str_mv	AT nathanwong kmeansquantizationforawebbasedopensourceflowcytometryanalysisplatform AT daehwankim kmeansquantizationforawebbasedopensourceflowcytometryanalysisplatform AT zacheryrobinson kmeansquantizationforawebbasedopensourceflowcytometryanalysisplatform AT conniehuang kmeansquantizationforawebbasedopensourceflowcytometryanalysisplatform AT irinamconboy kmeansquantizationforawebbasedopensourceflowcytometryanalysisplatform
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K-means quantization for a web-based open-source flow cytometry analysis platform

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