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erp pca matlab-based toolbox  (MathWorks Inc)


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    MathWorks Inc erp pca matlab-based toolbox
    Erp Pca Matlab Based Toolbox, 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/product/matlab-based+pca/pm39180166-165-20-19
    Average 90 stars, based on 1 article reviews
    erp pca matlab-based toolbox - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: A phylogenetically-conserved axis of thalamocortical connectivity in the human brain.
    Article Snippet: To assess the potential impact of the decomposition approach and the data used on our observations, we performed a series of sensitivity analyses: • Performed the joint decomposition using a nonlinear alternative to PCA (diffusion embedding using the BrainSpace MATLAB toolbox; version: 0.1.10; https://brainspace.readthedocs.io/en/ latest/index.html)26,54 to test if our observations were limited by using a linear model. • Performed the PCA using only human homologues of genes previously identified as differentially expressed along the medial-lateral axis in the mouse14 to test if the results were consistent when a more restricted gene-set was used.

    Article Title: Diffuse alveolar damage patterns reflect the immunological and molecular heterogeneity in fatal COVID-19.
    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.8 The markers having the most influence in the separation of DAD-associated clusters were identified by loading information.

    Article Title: Quantitative Structure-Property Relationship of the Rare-Earth Elements-Dibutyl Dithiophosphate Derivative Complexes Using Principal Component Analysis
    Article Snippet: Indraprasta PGRI University, Jakarta, 12530, Indonesia Department of Chemistry and Chemistry Institute for Functional Materials, Pusan National University, Busan, 46241, Republic of Korea Faculty of Mathematics and Natural Sciences, Padjadjaran University, Jatinangor, 45363, Indonesia Department of Energy Conversion Engineering, Politeknik Negeri Bandung, 40559, Indonesia Email: heruagungsaputra@pusan.ac.kr

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

    Article Title: Processing of task-irrelevant sounds during typical everyday activities in children
    Article Snippet: PCA was performed using the MATLAB-based ERP PCA toolkit ( ).

    Article Title: Geochemical trends in sedimentary environments using PCA approach
    Article Snippet: Investigating the geochemical composition of bulk sediments stands as a crucial method for unraveling the complexities of various sedimentary processes.. However, the intricacies arising from extensive datasets and alterations in sediment due to diverse factors often impede the clear identiBcation of underlying patterns in geochemical Cuctuations.. In addressing these, employing multivariate statistical analyses has proven to be an invaluable tool for elucidating intricate patterns within large dataset.

    Software:

    Article Title: Drastic Gas Sensing Selectivity in 2-Dimensional MoS 2 Nanoflakes by Noble Metal Decoration.
    Article Snippet: Noble metal nanoparticle decoration is a representative strategy to enhance selectivity for fabricating chemical sensor arrays based on the 2-dimensional (2D) semiconductor material, represented by molybdenum disulfide (MoS2).. However, the mechanism of selectivity tuning by noble metal decoration on 2D materials has not been fully elucidated.. Here, we successfully decorated noble metal nanoparticles on MoS2 flakes by the solution process without using reducing agents.



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    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.
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    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.
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    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.
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    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