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matlab-based easyflow  (MathWorks Inc)


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    Structured Review

    MathWorks Inc matlab-based easyflow
    Comparison of currently available software for flow cytometry analysis.
    Matlab Based Easyflow, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/matlab-based easyflow/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-based easyflow - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)"

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

    Journal: PLOS ONE

    doi: 10.1371/journal.pone.0308873

    Comparison of currently available software for flow cytometry analysis.
    Figure Legend Snippet: Comparison of currently available software for flow cytometry analysis.

    Techniques Used: Comparison, Software, Flow Cytometry

    Screenshots of the EasyFlow (Matlab) (A) and EasyFlowQ (Python) (B), with basic functions including managing FCS files, plotting and gating, annotated.
    Figure Legend Snippet: Screenshots of the EasyFlow (Matlab) (A) and EasyFlowQ (Python) (B), with basic functions including managing FCS files, plotting and gating, annotated.

    Techniques Used:

    T cells (Modified Jurkats, see ) were co-cultured with antigen-presenting cells (T2 line, B cells) and their cognate peptide to induce T cell activation. Cells were stained with antibodies to CD19 (B cell marker), CD3 (T cell receptor subunit), and CD69 (T cell activation marker). To gate live cells (A) and single cells (B), cells are plotted using the “Colored Dot Plot’’ option to visualize cell density and identify the sub-populations in the data. Cells are gated using a polygonal 2-dimensional gate, allowing to set the required gate to select for the desired population of cells. Next, a histogram display is used to identify the T cells and remove the B cells (C) and to identify T cells expressing CD3 that can respond to the added peptide (D). Using a 1-dimensional gate, we select the desired cells by choosing the range of values for the corresponding marker within a bi-modal population. In EasyFlow, gates are defined globally so that even if created for a single sample, gates can be applied to all samples in the analysis. In this way, the sequence of gates is applied to all samples in the analysis, enabling the comparison between different conditions. Finally, the percentage of activated cells as determined by the expression of CD69 is examined on the gated live single peptide-sensitive T cells. The percentage of CD69-expressing cells under three conditions: low, high, and no added peptide is examined (E). In all panels, the top row shows the EasyFlow (Matlab) UI, while the bottom row shows the EasyFlowQ (Python) UI.
    Figure Legend Snippet: T cells (Modified Jurkats, see ) were co-cultured with antigen-presenting cells (T2 line, B cells) and their cognate peptide to induce T cell activation. Cells were stained with antibodies to CD19 (B cell marker), CD3 (T cell receptor subunit), and CD69 (T cell activation marker). To gate live cells (A) and single cells (B), cells are plotted using the “Colored Dot Plot’’ option to visualize cell density and identify the sub-populations in the data. Cells are gated using a polygonal 2-dimensional gate, allowing to set the required gate to select for the desired population of cells. Next, a histogram display is used to identify the T cells and remove the B cells (C) and to identify T cells expressing CD3 that can respond to the added peptide (D). Using a 1-dimensional gate, we select the desired cells by choosing the range of values for the corresponding marker within a bi-modal population. In EasyFlow, gates are defined globally so that even if created for a single sample, gates can be applied to all samples in the analysis. In this way, the sequence of gates is applied to all samples in the analysis, enabling the comparison between different conditions. Finally, the percentage of activated cells as determined by the expression of CD69 is examined on the gated live single peptide-sensitive T cells. The percentage of CD69-expressing cells under three conditions: low, high, and no added peptide is examined (E). In all panels, the top row shows the EasyFlow (Matlab) UI, while the bottom row shows the EasyFlowQ (Python) UI.

    Techniques Used: Modification, Cell Culture, Activation Assay, Staining, Marker, Expressing, Sequencing, Comparison



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    matlab-based easyflow  (MathWorks Inc)
    90
    MathWorks Inc matlab-based easyflow
    Comparison of currently available software for flow cytometry analysis.
    Matlab Based Easyflow, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/matlab-based easyflow/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-based easyflow - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Comparison of currently available software for flow cytometry analysis.

    Journal: PLOS ONE

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

    doi: 10.1371/journal.pone.0308873

    Figure Lengend Snippet: Comparison of currently available software for flow cytometry analysis.

    Article Snippet: Here we present the Matlab-based EasyFlow ( github.com/AntebiLab/easyflow ) and its derivative standalone Python EasyFlowQ ( ym3141.github.io/EasyFlowQ/ ), which are open source user-friendly GUI, can be run on multiple platforms (Windows, MacOS and Linux), and require no coding knowledge.

    Techniques: Comparison, Software, Flow Cytometry

    Screenshots of the EasyFlow (Matlab) (A) and EasyFlowQ (Python) (B), with basic functions including managing FCS files, plotting and gating, annotated.

    Journal: PLOS ONE

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

    doi: 10.1371/journal.pone.0308873

    Figure Lengend Snippet: Screenshots of the EasyFlow (Matlab) (A) and EasyFlowQ (Python) (B), with basic functions including managing FCS files, plotting and gating, annotated.

    Article Snippet: Here we present the Matlab-based EasyFlow ( github.com/AntebiLab/easyflow ) and its derivative standalone Python EasyFlowQ ( ym3141.github.io/EasyFlowQ/ ), which are open source user-friendly GUI, can be run on multiple platforms (Windows, MacOS and Linux), and require no coding knowledge.

    Techniques:

    T cells (Modified Jurkats, see ) were co-cultured with antigen-presenting cells (T2 line, B cells) and their cognate peptide to induce T cell activation. Cells were stained with antibodies to CD19 (B cell marker), CD3 (T cell receptor subunit), and CD69 (T cell activation marker). To gate live cells (A) and single cells (B), cells are plotted using the “Colored Dot Plot’’ option to visualize cell density and identify the sub-populations in the data. Cells are gated using a polygonal 2-dimensional gate, allowing to set the required gate to select for the desired population of cells. Next, a histogram display is used to identify the T cells and remove the B cells (C) and to identify T cells expressing CD3 that can respond to the added peptide (D). Using a 1-dimensional gate, we select the desired cells by choosing the range of values for the corresponding marker within a bi-modal population. In EasyFlow, gates are defined globally so that even if created for a single sample, gates can be applied to all samples in the analysis. In this way, the sequence of gates is applied to all samples in the analysis, enabling the comparison between different conditions. Finally, the percentage of activated cells as determined by the expression of CD69 is examined on the gated live single peptide-sensitive T cells. The percentage of CD69-expressing cells under three conditions: low, high, and no added peptide is examined (E). In all panels, the top row shows the EasyFlow (Matlab) UI, while the bottom row shows the EasyFlowQ (Python) UI.

    Journal: PLOS ONE

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

    doi: 10.1371/journal.pone.0308873

    Figure Lengend Snippet: T cells (Modified Jurkats, see ) were co-cultured with antigen-presenting cells (T2 line, B cells) and their cognate peptide to induce T cell activation. Cells were stained with antibodies to CD19 (B cell marker), CD3 (T cell receptor subunit), and CD69 (T cell activation marker). To gate live cells (A) and single cells (B), cells are plotted using the “Colored Dot Plot’’ option to visualize cell density and identify the sub-populations in the data. Cells are gated using a polygonal 2-dimensional gate, allowing to set the required gate to select for the desired population of cells. Next, a histogram display is used to identify the T cells and remove the B cells (C) and to identify T cells expressing CD3 that can respond to the added peptide (D). Using a 1-dimensional gate, we select the desired cells by choosing the range of values for the corresponding marker within a bi-modal population. In EasyFlow, gates are defined globally so that even if created for a single sample, gates can be applied to all samples in the analysis. In this way, the sequence of gates is applied to all samples in the analysis, enabling the comparison between different conditions. Finally, the percentage of activated cells as determined by the expression of CD69 is examined on the gated live single peptide-sensitive T cells. The percentage of CD69-expressing cells under three conditions: low, high, and no added peptide is examined (E). In all panels, the top row shows the EasyFlow (Matlab) UI, while the bottom row shows the EasyFlowQ (Python) UI.

    Article Snippet: Here we present the Matlab-based EasyFlow ( github.com/AntebiLab/easyflow ) and its derivative standalone Python EasyFlowQ ( ym3141.github.io/EasyFlowQ/ ), which are open source user-friendly GUI, can be run on multiple platforms (Windows, MacOS and Linux), and require no coding knowledge.

    Techniques: Modification, Cell Culture, Activation Assay, Staining, Marker, Expressing, Sequencing, Comparison