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In PCA, time series of major principal component scores can be used to examine the temporal dynamics of the multivariate trajectory.
This was performed by comparing the values of genes along the major principal component axis (PC-1) in mRNA and protein dynamics data.
Our study is the first to reveal that the pattern of covariance in adipocytokine abundance, as clustered in the first major principal component, was not only highly predictive of body mass index, but also significantly transmitted from parent to offspring.
Figure 4 shows a scatterplot of all CPR proteins along the two major principal component axes, which together explain 57% of the total variation.
Environmental variation across lakes was summarized by three major principal component axes, which together accounted for 87% of the variation (Table C1 in Appendix S3).
Using principle components analysis, as implemented in SVS (Golden Helix Inc., Bozeman, MT), we selected the first major principal component eigenvalue to present the population structure based on the scree plot.
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In this study, for PCA, FA, and CA metal datasets, three major principal components and three major clusters were formed.
From PCA, five major principal components were extracted which perfectly reduced the data dimension and indicated possible anthropogenic sources.
From PCA, three major principal components were extracted which perfectly reduced the data dimension and also indicated possible anthropogenic sources.
Gaussian mixture model (GMM) is utilized to approximate the density distribution of the lower-dimensional feature space which consists of the major principal components.
Principal Component Analysis was applied on a significant number of variables containing relevant information of power quality and design features of the substations and storing the major Principal Components Scores.
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