<p>There was a pattern among the data separating subject 202 and 220 (black circles) from the rest of the subjects (grey circles).</p
<p>Principal Component Analysis (PCA) results on all individual samples at the level of OTUs cluster...
<p><b>A</b> Pareto plot of the percentage of variance explained by each principal component when PCA...
The UV-scaled PCA had two significant components where the first and second components explained 12%...
<p>Scatter plot of individuals, showing the first two principal components. Each symbol corresponds ...
<p>Principal component analysis (PCA) score plot between the low and high MEE groups.</p
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
<p>This is the correlation circle of the projection of the 45 variables using the first two componen...
<p>The scores plot generated from principal component analysis (PCA) of all the analytes (A) and the...
<p>Note: Each colored point represents a sample. The first, second and third principal components ar...
<p>Principal component analysis (PCA) scores plot from low, intermediate and high MEE groups.</p
<p>Each PCA vector represents a specific combination of the 1656 chemical descriptors. The three mos...
The figure shows the first two principal components after merging the acquired datasets. The samples...
<p>The principal component analysis was performed on all samples, and on the top 50 microRNAs with t...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
PBMC profiles obtained are shown in (A) and (B). Serum data are shown in (C) and (D). Individual plo...
<p>Principal Component Analysis (PCA) results on all individual samples at the level of OTUs cluster...
<p><b>A</b> Pareto plot of the percentage of variance explained by each principal component when PCA...
The UV-scaled PCA had two significant components where the first and second components explained 12%...
<p>Scatter plot of individuals, showing the first two principal components. Each symbol corresponds ...
<p>Principal component analysis (PCA) score plot between the low and high MEE groups.</p
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
<p>This is the correlation circle of the projection of the 45 variables using the first two componen...
<p>The scores plot generated from principal component analysis (PCA) of all the analytes (A) and the...
<p>Note: Each colored point represents a sample. The first, second and third principal components ar...
<p>Principal component analysis (PCA) scores plot from low, intermediate and high MEE groups.</p
<p>Each PCA vector represents a specific combination of the 1656 chemical descriptors. The three mos...
The figure shows the first two principal components after merging the acquired datasets. The samples...
<p>The principal component analysis was performed on all samples, and on the top 50 microRNAs with t...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
PBMC profiles obtained are shown in (A) and (B). Serum data are shown in (C) and (D). Individual plo...
<p>Principal Component Analysis (PCA) results on all individual samples at the level of OTUs cluster...
<p><b>A</b> Pareto plot of the percentage of variance explained by each principal component when PCA...
The UV-scaled PCA had two significant components where the first and second components explained 12%...