<p>Biplots show the correlation vectors representing the projection (in 2 dimensions) of loading for each of the variables (for 27 participants) included in the particular PCA. Vectors project onto the 3 axes (dimensions) of the principal components (PCs) 1 (x-axis), 2 (y-axis), and 3 (z-axis, positive is upward, perpendicular to the page). A. PCA-1, on the full dataset (N = 23 variables). B. PCA-2, Supraspinatus variables only (N = 16). C. PCA-3, variables of interest from previous comparisons, including “muscle” as a variable (N = 15 variables). Variables that loaded onto PC3 (as shown in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162494#pone.0162494.t001" target="_blank">1</a>–<a href="http://www.ploson...
<p>(A) Principal component analysis (PCA). The 32 samples are projected onto the 2D plane such that ...
<p>Principal component analysis (PCA) based on trunk sprouts in natural sites (NH) and disturbed sit...
<p>The distribution of the gene expression values shows evident trends represented by the vectors as...
<p>PCA biplots aim to optimally display variances and not correlations. The angles between the vario...
<p>The first and second axes of the PCA are shown. The length of the variable arrow corresponds to t...
<p>In the row for each variable, numbers indicate the strength of correlation of that variable with ...
Orthal cases 4) in M1 5) in M2 and 6) in M3. The loadings for each variable are coloured according s...
<p>PCA using normal esophagus (n = 25) and EoE samples (n = 11 untreated and n = 26 treated). The bi...
<p>The black circles show the sample data projected onto the first and second principal components. ...
<p>Agglomerative hierarchical clustering (AHC) computation based on Ward's method (right panel).</p
<p>PCA axes 1 and 2 account for 84.2% of the cumulative variance. The cluster analysis of the two ma...
Principle Component Analysis (PCA) is a powerful tool used in the field of statistics. In a given or...
<p>Global, multivariate correlation analysis. On the Biplot each body location is represented by pol...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>Principal components analysis of the 4 categories of interest show distinct clusters that corresp...
<p>(A) Principal component analysis (PCA). The 32 samples are projected onto the 2D plane such that ...
<p>Principal component analysis (PCA) based on trunk sprouts in natural sites (NH) and disturbed sit...
<p>The distribution of the gene expression values shows evident trends represented by the vectors as...
<p>PCA biplots aim to optimally display variances and not correlations. The angles between the vario...
<p>The first and second axes of the PCA are shown. The length of the variable arrow corresponds to t...
<p>In the row for each variable, numbers indicate the strength of correlation of that variable with ...
Orthal cases 4) in M1 5) in M2 and 6) in M3. The loadings for each variable are coloured according s...
<p>PCA using normal esophagus (n = 25) and EoE samples (n = 11 untreated and n = 26 treated). The bi...
<p>The black circles show the sample data projected onto the first and second principal components. ...
<p>Agglomerative hierarchical clustering (AHC) computation based on Ward's method (right panel).</p
<p>PCA axes 1 and 2 account for 84.2% of the cumulative variance. The cluster analysis of the two ma...
Principle Component Analysis (PCA) is a powerful tool used in the field of statistics. In a given or...
<p>Global, multivariate correlation analysis. On the Biplot each body location is represented by pol...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>Principal components analysis of the 4 categories of interest show distinct clusters that corresp...
<p>(A) Principal component analysis (PCA). The 32 samples are projected onto the 2D plane such that ...
<p>Principal component analysis (PCA) based on trunk sprouts in natural sites (NH) and disturbed sit...
<p>The distribution of the gene expression values shows evident trends represented by the vectors as...