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principle component analysis (pca) function  (MathWorks Inc)


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    MathWorks Inc principle component analysis (pca) function
    Principle Component Analysis (Pca) 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/principle+component+analysis+function/pm31206794-101-29-35
    Average 90 stars, based on 1 article reviews
    principle component analysis (pca) function - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Novel glauconite compounds improve soil properties and sugar beet ( Beta vulgaris L.) yields in saline soils
    Article Snippet: The multivariate statistics using principal component analysis (PCA), non-metric multidimensional scaling (NMDS) analysis, and self-organizing maps (SOM) have been carried out using MATLAB software (v. R2022a, The MathWorks, Natick, MA, USA).

    Article Title: Distinguishing impacts of climate change and human activities on phytoplankton communities in remote coastal waters: A paleoecological perspective.
    Article Snippet: Distinguishing between climatic and anthropogenic influences to accurately assess their respective impacts on coastal phytoplankton dynamics remains a complex challenge.. Northwestern Australia (NWA), largely free from direct human disturbances, presents a unique natural laboratory for disentangling these factors.. This study reconstructs phytoplankton community dynamics over the past century by analyzing sedimentary diatoms and silicoflagellates from sediment cores in Roebuck Bay, which experiences mild eutrophication, and Cygnet Bay, which remains relatively pristine.

    Article Title: Assessment of toxic polycyclic aromatic hydrocarbons (PAH) in recycled plastics: A comparative study of LDPE, HDPE, PET, and PP
    Article Snippet: In this work, a Principal Component Analysis (PCA) was carried out in order to identify correlations between the analysed parameters, using MATLAB (r2021).

    Article Title: Variability of antioxidant compounds and capacities in four wild fruits collected in different years and locations in Mozambique
    Article Snippet: Principal component analysis (PCA) was performed using MATLAB software (MATLAB and Statistics Toolbox Release R2022a, The MathWorks, Inc., Natick, Massachusetts, United States) to explore the relationships among TSS, TPC, TFC, vitamin C, TAC, temperature, and rainfall.

    Article Title: Unstable foveation’s impact on reading, object tracking, and its implications for diagnosing and intervening in reading difficulties
    Article Snippet: To explore the relationship between reading variables and catch-up saccades (CS) per second, a Principal Component Analysis (PCA) was applied to a set of oculomotor features using the ` pca ` function in MATLAB.

    Article Title: The effect of follicular and ampullary fluid extracellular vesicles on bovine oocyte competence and in vitro fertilization rates.
    Article Snippet: The data obtained from Raman microscopy were subjected to Principal Component Analysis (PCA) and PCA-Linear Discriminant Analysis (PCA-LDA) in MATLAB.

    Article Title: Chemical profiling of surviving cancer cells using ToF-SIMS and MCR analysis discriminates cell components.
    Article Snippet: The dataset included 190 negative ions, which were then subjected to Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) using MATLAB.



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    a Heat-map representation of viral gene expression levels (Et value, color scale bar) of 25 viral genes (x axis, infection phase and gene number) at 37 environmental samples, specifying station number and water depth (y axis), arranged by hierarchical clustering. b <t>Principle</t> <t>component</t> analysis <t>(PCA)</t> of the viral expression values for each environmental sample plotted over the axes of 1st component (PC1) vs. 2nd component (PC2). Samples are marked in a blue “+” or a red “x” according to the hierarchical clustering in the heat-map (a). c Coefficient values of viral genes for PC1 (x axis) and PC2 (y axis). The labels “Early” in blue, “Mid” in green, and “Late” in red represent the association of each viral gene to different phases of infection based on transcriptomic analysis [23, 24]. The gene number is depicted according to Supplementary Table S3
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    MathWorks Inc pca principle component analysis function
    a Heat-map representation of viral gene expression levels (Et value, color scale bar) of 25 viral genes (x axis, infection phase and gene number) at 37 environmental samples, specifying station number and water depth (y axis), arranged by hierarchical clustering. b <t>Principle</t> <t>component</t> analysis <t>(PCA)</t> of the viral expression values for each environmental sample plotted over the axes of 1st component (PC1) vs. 2nd component (PC2). Samples are marked in a blue “+” or a red “x” according to the hierarchical clustering in the heat-map (a). c Coefficient values of viral genes for PC1 (x axis) and PC2 (y axis). The labels “Early” in blue, “Mid” in green, and “Late” in red represent the association of each viral gene to different phases of infection based on transcriptomic analysis [23, 24]. The gene number is depicted according to Supplementary Table S3
    Pca Principle Component Analysis 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
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    MathWorks Inc software functions such as a principle component analysis (pca) and a non-negative least squares fit
    a Heat-map representation of viral gene expression levels (Et value, color scale bar) of 25 viral genes (x axis, infection phase and gene number) at 37 environmental samples, specifying station number and water depth (y axis), arranged by hierarchical clustering. b <t>Principle</t> <t>component</t> analysis <t>(PCA)</t> of the viral expression values for each environmental sample plotted over the axes of 1st component (PC1) vs. 2nd component (PC2). Samples are marked in a blue “+” or a red “x” according to the hierarchical clustering in the heat-map (a). c Coefficient values of viral genes for PC1 (x axis) and PC2 (y axis). The labels “Early” in blue, “Mid” in green, and “Late” in red represent the association of each viral gene to different phases of infection based on transcriptomic analysis [23, 24]. The gene number is depicted according to Supplementary Table S3
    Software Functions Such As A Principle Component Analysis (Pca) And A Non Negative Least Squares Fit, 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/principle+component+analysis+function/us09220411-196-25-13
    Average 90 stars, based on 1 article reviews
    software functions such as a principle component analysis (pca) and a non-negative least squares fit - by Bioz Stars, 2026-10
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    a Heat-map representation of viral gene expression levels (Et value, color scale bar) of 25 viral genes (x axis, infection phase and gene number) at 37 environmental samples, specifying station number and water depth (y axis), arranged by hierarchical clustering. b Principle component analysis (PCA) of the viral expression values for each environmental sample plotted over the axes of 1st component (PC1) vs. 2nd component (PC2). Samples are marked in a blue “+” or a red “x” according to the hierarchical clustering in the heat-map (a). c Coefficient values of viral genes for PC1 (x axis) and PC2 (y axis). The labels “Early” in blue, “Mid” in green, and “Late” in red represent the association of each viral gene to different phases of infection based on transcriptomic analysis [23, 24]. The gene number is depicted according to Supplementary Table S3

    Journal: The ISME Journal

    Article Title: Expression profiling of host and virus during a coccolithophore bloom provides insights into the role of viral infection in promoting carbon export

    doi: 10.1038/s41396-017-0004-x

    Figure Lengend Snippet: a Heat-map representation of viral gene expression levels (Et value, color scale bar) of 25 viral genes (x axis, infection phase and gene number) at 37 environmental samples, specifying station number and water depth (y axis), arranged by hierarchical clustering. b Principle component analysis (PCA) of the viral expression values for each environmental sample plotted over the axes of 1st component (PC1) vs. 2nd component (PC2). Samples are marked in a blue “+” or a red “x” according to the hierarchical clustering in the heat-map (a). c Coefficient values of viral genes for PC1 (x axis) and PC2 (y axis). The labels “Early” in blue, “Mid” in green, and “Late” in red represent the association of each viral gene to different phases of infection based on transcriptomic analysis [23, 24]. The gene number is depicted according to Supplementary Table S3

    Article Snippet: Principle component analysis was conducted on normalized Et values using the principle component analysis (PCA) function in MATLAB (MathWorks).

    Techniques: Gene Expression, Infection, Environmental Sampling, Expressing