<p>The figure represents the projection of the 48 arrays on the first three Principal Components. The sum of these principal three axes corresponds to 64.5% of the total variance. The representation corresponds to: The Rahman strain in contact with mucus (Red), Rahman strain in axenic culture (Blue), HMI:IMSS strain in contact with mucus (Green), and HMI:IMSS strain in axenic culture (Purple).</p
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>Cumulative explained variance and the number of principal components are shown for each movement ...
<p>Plotting of two principal axes in principal component analysis showed the variation among 21 bact...
<p>a) Graphic plot of the principal component analysis of 183 maize inbred lines, calculated from ~3...
<p>The distribution of the strains in the plane formed by Principal Component 1 and Principal Compon...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Numbers in parentheses represent the percentage of total variance explained by the first and seco...
<p>Curves show the cumulative sum of variance explained by increasing numbers of principal component...
<p>The axes of the plot represent first two main components of variance, obtained by performing prin...
<p>Principal components analysis of size-regressed measurements, with loadings of each measurement f...
The PCA is based on the 30 samples from the discovery set and 98 CEU controls extracted from the 100...
<p>(a) Variation explained by Principal Components. We retain the first four Principal Components wh...
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>Cumulative explained variance and the number of principal components are shown for each movement ...
<p>Plotting of two principal axes in principal component analysis showed the variation among 21 bact...
<p>a) Graphic plot of the principal component analysis of 183 maize inbred lines, calculated from ~3...
<p>The distribution of the strains in the plane formed by Principal Component 1 and Principal Compon...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Numbers in parentheses represent the percentage of total variance explained by the first and seco...
<p>Curves show the cumulative sum of variance explained by increasing numbers of principal component...
<p>The axes of the plot represent first two main components of variance, obtained by performing prin...
<p>Principal components analysis of size-regressed measurements, with loadings of each measurement f...
The PCA is based on the 30 samples from the discovery set and 98 CEU controls extracted from the 100...
<p>(a) Variation explained by Principal Components. We retain the first four Principal Components wh...
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...