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function implementation of principal component analysis (pca)  (MathWorks Inc)


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    MathWorks Inc function implementation of principal component analysis (pca)
    Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a <t>PCA</t> analysis of the in silico copy number data, shown as a biplot of the first two <t>principal</t> <t>components.</t> Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.
    Function Implementation Of 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/product/function+implementation+of+principal+component+analysis+(pca)/pmc07846089-72-15-11
    Average 90 stars, based on 1 article reviews
    function implementation of principal component analysis (pca) - by Bioz Stars, 2026-10
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    1) Product Images from "In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity"

    Article Title: In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

    Journal: NAR Genomics and Bioinformatics

    doi: 10.1093/nargab/lqaa112

    Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a PCA analysis of the in silico copy number data, shown as a biplot of the first two principal components. Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.
    Figure Legend Snippet: Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a PCA analysis of the in silico copy number data, shown as a biplot of the first two principal components. Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.

    Techniques Used: In Silico, Genome Wide, DNA Amplification, Activity Assay, Amplification

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    function implementation of principal component analysis (pca)  (MathWorks Inc)
    90
    MathWorks Inc function implementation of principal component analysis (pca)
    Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a <t>PCA</t> analysis of the in silico copy number data, shown as a biplot of the first two <t>principal</t> <t>components.</t> Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.
    Function Implementation Of 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/product/function+implementation+of+principal+component+analysis+(pca)/pmc07846089-72-15-11
    Average 90 stars, based on 1 article reviews
    function implementation of principal component analysis (pca) - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

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    Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a PCA analysis of the in silico copy number data, shown as a biplot of the first two principal components. Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.

    Journal: NAR Genomics and Bioinformatics

    Article Title: In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

    doi: 10.1093/nargab/lqaa112

    Figure Lengend Snippet: Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a PCA analysis of the in silico copy number data, shown as a biplot of the first two principal components. Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.

    Article Snippet: To compute the principal components of the data we used the MATLAB function implementation of Principal Component Analysis (PCA) and visualized the results (variable loadings and principal components) using a biplot.

    Techniques: In Silico, Genome Wide, DNA Amplification, Activity Assay, Amplification