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matlab-based principal component analysis (pca)  (MathWorks Inc)


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    MathWorks Inc matlab-based principal component analysis (pca)
    Decoding of cell marker compositions <t>during</t> <t>DAD</t> progression . (A-C) Zoomed-in micrographs of diffuse alveolar damage (DAD) patterns, as viewed by traditional H&E staining and corresponding multiplex immunohistochemistry. Panels A-C show paired H&E and multiplex images and typical patterns during exudative, intermediate, and advanced DAD, respectively. (D-E) Quantitative data on the density of immune cells (D) and structural cell markers (E) across the DAD patterns. The data are from marker density analysis in 95 multiplex-stained tissue regions of interest (ROIs) that were selected from H&E-stained sections with the criteria of having a uniform and distinct DAD histopathology. Statistical comparisons were determined by a non-parametric Kruskal–Wallis test, followed by Bonferroni post-hoc test. (F) Multivariate analysis of individual DAD region marker content and identification of 3 clusters of marker constellations by principal component analysis <t>(PCA)</t> and unsupervised K-mean clustering (Clusters 1-3). Individual ROIs within the PCA-defined clusters are color-coded according to previously H&E-confirmed DAD patterns. (G) Individual ROIs sorted for increasing abundance of the 4 markers that had the most statistical influence on initial cluster identification, again individual ROIs are color-coded according to DAD category. (H) Cell plots with relative marker densities across the identified clusters. Each horizontal line represents one DAD region; with its DAD category color-coding to the right. * p <0·05 and ** p <0·01.
    Matlab Based Principal Component Analysis (Pca), 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 principal component analysis (pca)/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-based principal component analysis (pca) - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Diffuse alveolar damage patterns reflect the immunological and molecular heterogeneity in fatal COVID-19"

    Article Title: Diffuse alveolar damage patterns reflect the immunological and molecular heterogeneity in fatal COVID-19

    Journal: eBioMedicine

    doi: 10.1016/j.ebiom.2022.104229

    Decoding of cell marker compositions during DAD progression . (A-C) Zoomed-in micrographs of diffuse alveolar damage (DAD) patterns, as viewed by traditional H&E staining and corresponding multiplex immunohistochemistry. Panels A-C show paired H&E and multiplex images and typical patterns during exudative, intermediate, and advanced DAD, respectively. (D-E) Quantitative data on the density of immune cells (D) and structural cell markers (E) across the DAD patterns. The data are from marker density analysis in 95 multiplex-stained tissue regions of interest (ROIs) that were selected from H&E-stained sections with the criteria of having a uniform and distinct DAD histopathology. Statistical comparisons were determined by a non-parametric Kruskal–Wallis test, followed by Bonferroni post-hoc test. (F) Multivariate analysis of individual DAD region marker content and identification of 3 clusters of marker constellations by principal component analysis (PCA) and unsupervised K-mean clustering (Clusters 1-3). Individual ROIs within the PCA-defined clusters are color-coded according to previously H&E-confirmed DAD patterns. (G) Individual ROIs sorted for increasing abundance of the 4 markers that had the most statistical influence on initial cluster identification, again individual ROIs are color-coded according to DAD category. (H) Cell plots with relative marker densities across the identified clusters. Each horizontal line represents one DAD region; with its DAD category color-coding to the right. * p <0·05 and ** p <0·01.
    Figure Legend Snippet: Decoding of cell marker compositions during DAD progression . (A-C) Zoomed-in micrographs of diffuse alveolar damage (DAD) patterns, as viewed by traditional H&E staining and corresponding multiplex immunohistochemistry. Panels A-C show paired H&E and multiplex images and typical patterns during exudative, intermediate, and advanced DAD, respectively. (D-E) Quantitative data on the density of immune cells (D) and structural cell markers (E) across the DAD patterns. The data are from marker density analysis in 95 multiplex-stained tissue regions of interest (ROIs) that were selected from H&E-stained sections with the criteria of having a uniform and distinct DAD histopathology. Statistical comparisons were determined by a non-parametric Kruskal–Wallis test, followed by Bonferroni post-hoc test. (F) Multivariate analysis of individual DAD region marker content and identification of 3 clusters of marker constellations by principal component analysis (PCA) and unsupervised K-mean clustering (Clusters 1-3). Individual ROIs within the PCA-defined clusters are color-coded according to previously H&E-confirmed DAD patterns. (G) Individual ROIs sorted for increasing abundance of the 4 markers that had the most statistical influence on initial cluster identification, again individual ROIs are color-coded according to DAD category. (H) Cell plots with relative marker densities across the identified clusters. Each horizontal line represents one DAD region; with its DAD category color-coding to the right. * p <0·05 and ** p <0·01.

    Techniques Used: Marker, Staining, Multiplex Assay, Immunohistochemistry, Histopathology



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    matlab-based principal component analysis (pca)  (MathWorks Inc)
    90
    MathWorks Inc matlab-based principal component analysis (pca)
    Decoding of cell marker compositions <t>during</t> <t>DAD</t> progression . (A-C) Zoomed-in micrographs of diffuse alveolar damage (DAD) patterns, as viewed by traditional H&E staining and corresponding multiplex immunohistochemistry. Panels A-C show paired H&E and multiplex images and typical patterns during exudative, intermediate, and advanced DAD, respectively. (D-E) Quantitative data on the density of immune cells (D) and structural cell markers (E) across the DAD patterns. The data are from marker density analysis in 95 multiplex-stained tissue regions of interest (ROIs) that were selected from H&E-stained sections with the criteria of having a uniform and distinct DAD histopathology. Statistical comparisons were determined by a non-parametric Kruskal–Wallis test, followed by Bonferroni post-hoc test. (F) Multivariate analysis of individual DAD region marker content and identification of 3 clusters of marker constellations by principal component analysis <t>(PCA)</t> and unsupervised K-mean clustering (Clusters 1-3). Individual ROIs within the PCA-defined clusters are color-coded according to previously H&E-confirmed DAD patterns. (G) Individual ROIs sorted for increasing abundance of the 4 markers that had the most statistical influence on initial cluster identification, again individual ROIs are color-coded according to DAD category. (H) Cell plots with relative marker densities across the identified clusters. Each horizontal line represents one DAD region; with its DAD category color-coding to the right. * p <0·05 and ** p <0·01.
    Matlab Based Principal Component Analysis (Pca), 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 principal component analysis (pca)/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-based principal component analysis (pca) - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Decoding of cell marker compositions during DAD progression . (A-C) Zoomed-in micrographs of diffuse alveolar damage (DAD) patterns, as viewed by traditional H&E staining and corresponding multiplex immunohistochemistry. Panels A-C show paired H&E and multiplex images and typical patterns during exudative, intermediate, and advanced DAD, respectively. (D-E) Quantitative data on the density of immune cells (D) and structural cell markers (E) across the DAD patterns. The data are from marker density analysis in 95 multiplex-stained tissue regions of interest (ROIs) that were selected from H&E-stained sections with the criteria of having a uniform and distinct DAD histopathology. Statistical comparisons were determined by a non-parametric Kruskal–Wallis test, followed by Bonferroni post-hoc test. (F) Multivariate analysis of individual DAD region marker content and identification of 3 clusters of marker constellations by principal component analysis (PCA) and unsupervised K-mean clustering (Clusters 1-3). Individual ROIs within the PCA-defined clusters are color-coded according to previously H&E-confirmed DAD patterns. (G) Individual ROIs sorted for increasing abundance of the 4 markers that had the most statistical influence on initial cluster identification, again individual ROIs are color-coded according to DAD category. (H) Cell plots with relative marker densities across the identified clusters. Each horizontal line represents one DAD region; with its DAD category color-coding to the right. * p <0·05 and ** p <0·01.

    Journal: eBioMedicine

    Article Title: Diffuse alveolar damage patterns reflect the immunological and molecular heterogeneity in fatal COVID-19

    doi: 10.1016/j.ebiom.2022.104229

    Figure Lengend Snippet: Decoding of cell marker compositions during DAD progression . (A-C) Zoomed-in micrographs of diffuse alveolar damage (DAD) patterns, as viewed by traditional H&E staining and corresponding multiplex immunohistochemistry. Panels A-C show paired H&E and multiplex images and typical patterns during exudative, intermediate, and advanced DAD, respectively. (D-E) Quantitative data on the density of immune cells (D) and structural cell markers (E) across the DAD patterns. The data are from marker density analysis in 95 multiplex-stained tissue regions of interest (ROIs) that were selected from H&E-stained sections with the criteria of having a uniform and distinct DAD histopathology. Statistical comparisons were determined by a non-parametric Kruskal–Wallis test, followed by Bonferroni post-hoc test. (F) Multivariate analysis of individual DAD region marker content and identification of 3 clusters of marker constellations by principal component analysis (PCA) and unsupervised K-mean clustering (Clusters 1-3). Individual ROIs within the PCA-defined clusters are color-coded according to previously H&E-confirmed DAD patterns. (G) Individual ROIs sorted for increasing abundance of the 4 markers that had the most statistical influence on initial cluster identification, again individual ROIs are color-coded according to DAD category. (H) Cell plots with relative marker densities across the identified clusters. Each horizontal line represents one DAD region; with its DAD category color-coding to the right. * p <0·05 and ** p <0·01.

    Article Snippet: The generated DAD ROI data were subjected to multivariate analysis using a MATLAB-based principal component analysis (PCA) and K-means exploration to identify cluster formation in the PCA plot.

    Techniques: Marker, Staining, Multiplex Assay, Immunohistochemistry, Histopathology