EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study developmental biology, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by the International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages has been developed for the analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding, or 3) online tools that are limiting for analysis of large data sets. Here, we present EasyFlow, an open-source flow cytometry analysis graphic user interface (GUI) based on Matlab or Python, that can be installed and run locally across platforms (Windows, MacOS, and Linux) without requiring previous coding knowledge. The Python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

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Institutions

Publication Details

Journal
PLoS ONE
Published
2024-11-13
DOI
https://doi.org/10.1371/journal.pone.0308873
Citations
6
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
1.40

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article

EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)

Yitong Ma, Inbal Eizenberg-Magar, Yaron E. Antebi
6 citations
PLoS ONE
Single-cell and spatial transcriptomics
1.40
article

EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)

Yitong Ma, Inbal Eizenberg-Magar, Yaron E. Antebi
article en
6 citations

Abstract

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study developmental biology, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by the International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages has been developed for the analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding, or 3) online tools that are limiting for analysis of large data sets. Here, we present EasyFlow, an open-source flow cytometry analysis graphic user interface (GUI) based on Matlab or Python, that can be installed and run locally across platforms (Windows, MacOS, and Linux) without requiring previous coding knowledge. The Python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

PLoS ONEVol. 19(11)
Weizmann Institute of Science (IL), Stanford University (US)
National Science Foundation, Cancer Research Institute, Israel Science Foundation, National Institutes of Health
Openalex Percentile: Top 18%
Single-cell and spatial transcriptomics
1.40
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