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non-negative matrix factorization matlab nnmf function  (MathWorks Inc)


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    MathWorks Inc non-negative matrix factorization matlab nnmf function
    Non Negative Matrix Factorization Matlab Nnmf Function, 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/non-negative+matrix+factorization+function+nnmf/pm40140582-769-20-23
    Average 90 stars, based on 1 article reviews
    non-negative matrix factorization matlab nnmf function - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: A System for Gene Expression Noise Control in Yeast
    Article Snippet: Up to a normalization, the set of values for a and b were chosen by non-negative matrix factorization (using the function nnmf in MATLAB) of the matrix of experimental mean fluorescent values (similar to Figure a).

    Article Title: Super-multiplexed fluorescence microscopy via photostability contrast
    Article Snippet: We perform NMF by using the built-in MATLAB non-negative matrix factorization function nnmf .

    Article Title: In silico prediction of high-resolution Hi-C interaction matrices
    Article Snippet: We use MATLAB’s non-negative matrix factorization function nnmf, with k = 5 factors to perform this factorization, which uses an iterative algorithm to estimate U and V . The U and V matrices provide a low-dimensional representation of the interaction pairs and feature pairs respectively.

    Article Title: Super-multiplexed fluorescence microscopy via photostability contrast
    Article Snippet: We perform NMF by using the built-in MATLAB non-negative matrix factorization function nnmf.

    Article Title: A system for gene expression noise control in yeast
    Article Snippet: Up to a normalization, the set of values for a and b were chosen by non‐negative matrix factorization (using the function nnmf in MATLAB) of the matrix of experimental mean fluorescent values (similar to ).

    Article Title: In silico prediction of high-resolution Hi-C interaction matrices
    Article Snippet: We use MATLAB’s non-negative matrix factorization function nnmf, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k=5$$\end{document} k = 5 factors to perform this factorization, which uses the alternating least squares algorithm to estimate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\bf{U}}$$\end{document} U and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\bf{V}}$$\end{document} V . The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\bf{U}}$$\end{document} U and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\bf{V}}$$\end{document} V matrices provide a low-dimensional representation of the interaction pairs and feature pairs respectively.



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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    non-negative matrix factorization function nnmf  (MathWorks Inc)
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Journal: Biomaterials and Biosystems

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy

    doi: 10.1016/j.bbiosy.2022.100059

    Figure Lengend Snippet: Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Article Snippet: Following pre-processing, spectral models were developed using the MATLAB statistics toolbox function non-negative matrix factorization (NNMF) ( c) with in vivo spectra ( a), ex vivo , and reference spectra ( b) as input.

    Techniques: Immunohistochemical staining, Raman Spectroscopy, Derivative Assay, Ex Vivo, In Vivo