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The percentage Principal Component Analysis (PCA) implemented using Multiple Co-inertia Analysis (MCoA) clustered populations of R. appendiculatus into 2 groups.
Construct validity, assessed using principal component analysis (PCA), showed high percentages of explained variance.
The contribution of each identified QTL to total phenotypic variance (R) or percentage of variance explained by the QTL (PVE) was estimated by variance component analysis.
Primer-E worksheets that detailed the presence and absence, as well as the percentage abundances of individual bacterial genera were collated, and subsequently performed with principal component analysis on Primer-E software (http://www.primer-e.com/).
Multivariate analysis of propolis chemical profiles was performed by principal component analysis (PCA), using the GC-MS data for the identified compounds expressed as a percentage of the Total Ion Current, respectively.
The percentages and total amounts of FAs in the three different cultures were included as variables in a principal component analysis (PCA).
Employing the Principal Component Analysis (PCA), the degree of correlation between the topoclimatic variables was evaluated selecting the representative variables in percentage of the data variance.
Principal component analysis (PCA) with simple Euclidean plane calculation was used for pattern recognition and evaluation of adulteration percentages.
Figure 5: Principal component analysis.
Principal component analysis with genome sequence data.
Principal component analysis of hg R1a subclades.
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