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matlab-based histo-cytometric multidimensional analysis pipeline  (MathWorks Inc)


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    Structured Review

    MathWorks Inc matlab-based histo-cytometric multidimensional analysis pipeline
    Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using <t>CytoMap.</t> (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.
    Matlab Based Histo Cytometric Multidimensional Analysis Pipeline, 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 histo-cytometric multidimensional analysis pipeline/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-based histo-cytometric multidimensional analysis pipeline - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Predicting IHC staining classes of NF1 using features in the hematoxylin channel"

    Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

    Journal: Journal of Pathology Informatics

    doi: 10.1016/j.jpi.2023.100196

    Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using CytoMap. (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.
    Figure Legend Snippet: Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using CytoMap. (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.

    Techniques Used: Staining, Generated, Extraction

    Cell clustering using CytoMap . (A) Schematic workflow using CytoMap . Cell-wise H&M feature values together with cell coordinates are entered into CytoMap. (B) CytoMap output . CytoMap identifies the optimal number of clusters in the data and provides each cell with a cluster label. The pink shaded areas represent the range of NF1 staining intensity in high-NF1 expressing tubules, while the gray shaded area indicates NF1 staining levels in low-NF1 tubules. (C) Cluster visualization . Cluster labels are retuned to QuPath for overlay with the original image.
    Figure Legend Snippet: Cell clustering using CytoMap . (A) Schematic workflow using CytoMap . Cell-wise H&M feature values together with cell coordinates are entered into CytoMap. (B) CytoMap output . CytoMap identifies the optimal number of clusters in the data and provides each cell with a cluster label. The pink shaded areas represent the range of NF1 staining intensity in high-NF1 expressing tubules, while the gray shaded area indicates NF1 staining levels in low-NF1 tubules. (C) Cluster visualization . Cluster labels are retuned to QuPath for overlay with the original image.

    Techniques Used: Staining, Expressing

    Cluster analysis of ccRCC, ChRCC, and PRCC . (A) Representative region of interest from cores analyzed in CytoMap. Columns 1 and 2 show corresponding H&E and IHC images after co-registration. The distance between the H&E and IHC tissue sections hinders a direct comparison at the cell level. The IHC image is analyzed in QuPath as described in to identify the NF1 class of each cell (third column). Fourth column shows the CytoMap cluster designation of each cell in the tissue context. (B) NF1 intensity comparison among clusters . Box plots show the NF1 staining intensity in each cluster. (C) Cluster representation inside NF1 classes. The percentage of cells within each cluster is plotted on the y-axis for NF1-negative, NF1-low, and NF1-high classes (X-axis) . Tissue images are screenshots at 40X magnification in QuPath.
    Figure Legend Snippet: Cluster analysis of ccRCC, ChRCC, and PRCC . (A) Representative region of interest from cores analyzed in CytoMap. Columns 1 and 2 show corresponding H&E and IHC images after co-registration. The distance between the H&E and IHC tissue sections hinders a direct comparison at the cell level. The IHC image is analyzed in QuPath as described in to identify the NF1 class of each cell (third column). Fourth column shows the CytoMap cluster designation of each cell in the tissue context. (B) NF1 intensity comparison among clusters . Box plots show the NF1 staining intensity in each cluster. (C) Cluster representation inside NF1 classes. The percentage of cells within each cluster is plotted on the y-axis for NF1-negative, NF1-low, and NF1-high classes (X-axis) . Tissue images are screenshots at 40X magnification in QuPath.

    Techniques Used: Comparison, Staining



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    matlab-based histo-cytometric multidimensional analysis pipeline  (MathWorks Inc)
    90
    MathWorks Inc matlab-based histo-cytometric multidimensional analysis pipeline
    Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using <t>CytoMap.</t> (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.
    Matlab Based Histo Cytometric Multidimensional Analysis Pipeline, 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 histo-cytometric multidimensional analysis pipeline/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-based histo-cytometric multidimensional analysis pipeline - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using CytoMap. (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.

    Journal: Journal of Pathology Informatics

    Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

    doi: 10.1016/j.jpi.2023.100196

    Figure Lengend Snippet: Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using CytoMap. (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.

    Article Snippet: CytoMap is an MatLab-based Histo-Cytometric Multidimensional Analysis Pipeline (CytoMap) for spatial analysis of segmented cell objects, which utilizes diverse statistical approaches to extract and quantify information about cellular spatial positioning, preferential cell–cell associations, and global tissue structure.

    Techniques: Staining, Generated, Extraction

    Cell clustering using CytoMap . (A) Schematic workflow using CytoMap . Cell-wise H&M feature values together with cell coordinates are entered into CytoMap. (B) CytoMap output . CytoMap identifies the optimal number of clusters in the data and provides each cell with a cluster label. The pink shaded areas represent the range of NF1 staining intensity in high-NF1 expressing tubules, while the gray shaded area indicates NF1 staining levels in low-NF1 tubules. (C) Cluster visualization . Cluster labels are retuned to QuPath for overlay with the original image.

    Journal: Journal of Pathology Informatics

    Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

    doi: 10.1016/j.jpi.2023.100196

    Figure Lengend Snippet: Cell clustering using CytoMap . (A) Schematic workflow using CytoMap . Cell-wise H&M feature values together with cell coordinates are entered into CytoMap. (B) CytoMap output . CytoMap identifies the optimal number of clusters in the data and provides each cell with a cluster label. The pink shaded areas represent the range of NF1 staining intensity in high-NF1 expressing tubules, while the gray shaded area indicates NF1 staining levels in low-NF1 tubules. (C) Cluster visualization . Cluster labels are retuned to QuPath for overlay with the original image.

    Article Snippet: CytoMap is an MatLab-based Histo-Cytometric Multidimensional Analysis Pipeline (CytoMap) for spatial analysis of segmented cell objects, which utilizes diverse statistical approaches to extract and quantify information about cellular spatial positioning, preferential cell–cell associations, and global tissue structure.

    Techniques: Staining, Expressing

    Cluster analysis of ccRCC, ChRCC, and PRCC . (A) Representative region of interest from cores analyzed in CytoMap. Columns 1 and 2 show corresponding H&E and IHC images after co-registration. The distance between the H&E and IHC tissue sections hinders a direct comparison at the cell level. The IHC image is analyzed in QuPath as described in to identify the NF1 class of each cell (third column). Fourth column shows the CytoMap cluster designation of each cell in the tissue context. (B) NF1 intensity comparison among clusters . Box plots show the NF1 staining intensity in each cluster. (C) Cluster representation inside NF1 classes. The percentage of cells within each cluster is plotted on the y-axis for NF1-negative, NF1-low, and NF1-high classes (X-axis) . Tissue images are screenshots at 40X magnification in QuPath.

    Journal: Journal of Pathology Informatics

    Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

    doi: 10.1016/j.jpi.2023.100196

    Figure Lengend Snippet: Cluster analysis of ccRCC, ChRCC, and PRCC . (A) Representative region of interest from cores analyzed in CytoMap. Columns 1 and 2 show corresponding H&E and IHC images after co-registration. The distance between the H&E and IHC tissue sections hinders a direct comparison at the cell level. The IHC image is analyzed in QuPath as described in to identify the NF1 class of each cell (third column). Fourth column shows the CytoMap cluster designation of each cell in the tissue context. (B) NF1 intensity comparison among clusters . Box plots show the NF1 staining intensity in each cluster. (C) Cluster representation inside NF1 classes. The percentage of cells within each cluster is plotted on the y-axis for NF1-negative, NF1-low, and NF1-high classes (X-axis) . Tissue images are screenshots at 40X magnification in QuPath.

    Article Snippet: CytoMap is an MatLab-based Histo-Cytometric Multidimensional Analysis Pipeline (CytoMap) for spatial analysis of segmented cell objects, which utilizes diverse statistical approaches to extract and quantify information about cellular spatial positioning, preferential cell–cell associations, and global tissue structure.

    Techniques: Comparison, Staining