<p>The first 3 principal components which account for most of the variance in the original data set are shown A) from MS-data of C8-beads sample preparation and B) from MS-data of C18-beads sample preparation; ALS patients (red) and healthy controls (green) ; 1 dot per patient (average of 9 spectra per patient); Eigenvalues screen plots are at the right of each PCA.</p
<p>Data were collected for a set of 6 markers; PGE2, PGE2-EP2, PGES, COX-2, cPLA2, and 8-isoprostane...
Principal components with its eigenvalues and percentage variances towards the total population vari...
Variance is primarily due to classic A-T phenotype (PC1). Red- Classic A-T phenotype (n = 3) and Blu...
<p>Principal Component Analysis score plot for ALS (n = 50) and non ALS patients (n = 44).</p
<p>A. Females with ALS overlapped with female controls in the measured parameters. (t <a href="http:...
<p>Set 1 (a) and 2 (b) data obtained from mild/moderate and severe autism groups were analyzed with ...
<p>Note: Each colored point represents a sample. The first, second and third principal components ar...
<p>a. In the discovery dataset, the 5 tumor and 1 control methylation classes were represented by th...
The x, y, and z axes are the first, second, and third components that together capture most of the v...
<p>The graph represents the number of PCA components considered and the related percentage of varian...
<p>Figure (A) shows the variance of each of the principal component scores amongst the 90 patients s...
<p>A: all individuals from Stage I and HapMap; B: breast cancer cases and controls from Stage I.</p
<p>Analysis of C18 data from ALS patients A/ spinal-onset (red) and bulbar-onset (turquoise); B/ fem...
<p>Principal component analysis (PCA) was used to reduce the dimension of the tertiary dataset compr...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
<p>Data were collected for a set of 6 markers; PGE2, PGE2-EP2, PGES, COX-2, cPLA2, and 8-isoprostane...
Principal components with its eigenvalues and percentage variances towards the total population vari...
Variance is primarily due to classic A-T phenotype (PC1). Red- Classic A-T phenotype (n = 3) and Blu...
<p>Principal Component Analysis score plot for ALS (n = 50) and non ALS patients (n = 44).</p
<p>A. Females with ALS overlapped with female controls in the measured parameters. (t <a href="http:...
<p>Set 1 (a) and 2 (b) data obtained from mild/moderate and severe autism groups were analyzed with ...
<p>Note: Each colored point represents a sample. The first, second and third principal components ar...
<p>a. In the discovery dataset, the 5 tumor and 1 control methylation classes were represented by th...
The x, y, and z axes are the first, second, and third components that together capture most of the v...
<p>The graph represents the number of PCA components considered and the related percentage of varian...
<p>Figure (A) shows the variance of each of the principal component scores amongst the 90 patients s...
<p>A: all individuals from Stage I and HapMap; B: breast cancer cases and controls from Stage I.</p
<p>Analysis of C18 data from ALS patients A/ spinal-onset (red) and bulbar-onset (turquoise); B/ fem...
<p>Principal component analysis (PCA) was used to reduce the dimension of the tertiary dataset compr...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
<p>Data were collected for a set of 6 markers; PGE2, PGE2-EP2, PGES, COX-2, cPLA2, and 8-isoprostane...
Principal components with its eigenvalues and percentage variances towards the total population vari...
Variance is primarily due to classic A-T phenotype (PC1). Red- Classic A-T phenotype (n = 3) and Blu...