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pearson-type principle component analysis (pca)  (Addinsoft inc)

 
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    Addinsoft inc pearson-type principle component analysis (pca)
    Pearson Type Principle Component Analysis (Pca), supplied by Addinsoft 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/pearson+type+principle+component+analysis++pca+/pm37812154-120-2-34
    Average 90 stars, based on 1 article reviews
    pearson-type principle component analysis (pca) - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Bioactive potential of ripened white cheeses manufactured in different geographical regions of Turkey.
    Article Snippet: Additionally, a Pearson-type Principle Component Analysis (PCA) that automatically standardizes or normalizes the data prior to computations to avoid inflating the impact of variables with high variances on the result was conducted using XLSTAT (Addinsoft, Paris, France).



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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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    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