<p>A. The figure shows the graphical representation of the first two eigenvectors after PCA analysis. Y-axis corresponds to the first vector explaining 24.1% of the variation and X-axis explains 13.4% of the remaining variation. Each dot represents the results from one individual and the colour represent each HG as denoted by letters in the figure. The plus symbols in black denote individuals for which HG determination was ambiguous. The black triangles denote individuals for which no HG could be assigned. B. The results from the individuals carrying the blue-grey dupl are represented in blue, while the results from individuals carrying “blue-grey like dupl.” are in red. All cases are included within the NO-M214(xM175) haplogroup. In total ...
Variance is primarily due to classic A-T phenotype (PC1). Red- Classic A-T phenotype (n = 3) and Blu...
The clusters are shown on the horizontal axis and the countries and SNP haplotypes are indicated on ...
<p>(A) The percent variability explained by each principal component (<a href="http://www.plosone.or...
<p>The two leading principal components display the variance. The superimposed biplot shows the cont...
Background : Principal Components Analysis is a standard and computationally efficient method to exp...
<p>Numbers in parentheses indicate the proportion of the total genetic information retained by a giv...
<p>The first two global components, sPC1 (a) and sPC2 (b), are depicted. Positive values are represe...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>The first two global components, sPC1 (a) and sPC2 (b), are depicted. Positive values are represe...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>The barplot represents DAPC-based posterior membership probabilities for each of the considered p...
<p>A) Principal component analysis of haplogroups frequencies. B) Multidimensional scaling plot base...
<p>Each part of the figure shows the graphical representation of the first two eigenvectors after PC...
<p>The first principal component accounts for 94.7% of variation and the second component explained ...
<p>A) PCA plot, in which 10 Japanese district populations are plotted according to their correspondi...
Variance is primarily due to classic A-T phenotype (PC1). Red- Classic A-T phenotype (n = 3) and Blu...
The clusters are shown on the horizontal axis and the countries and SNP haplotypes are indicated on ...
<p>(A) The percent variability explained by each principal component (<a href="http://www.plosone.or...
<p>The two leading principal components display the variance. The superimposed biplot shows the cont...
Background : Principal Components Analysis is a standard and computationally efficient method to exp...
<p>Numbers in parentheses indicate the proportion of the total genetic information retained by a giv...
<p>The first two global components, sPC1 (a) and sPC2 (b), are depicted. Positive values are represe...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>The first two global components, sPC1 (a) and sPC2 (b), are depicted. Positive values are represe...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>The barplot represents DAPC-based posterior membership probabilities for each of the considered p...
<p>A) Principal component analysis of haplogroups frequencies. B) Multidimensional scaling plot base...
<p>Each part of the figure shows the graphical representation of the first two eigenvectors after PC...
<p>The first principal component accounts for 94.7% of variation and the second component explained ...
<p>A) PCA plot, in which 10 Japanese district populations are plotted according to their correspondi...
Variance is primarily due to classic A-T phenotype (PC1). Red- Classic A-T phenotype (n = 3) and Blu...
The clusters are shown on the horizontal axis and the countries and SNP haplotypes are indicated on ...
<p>(A) The percent variability explained by each principal component (<a href="http://www.plosone.or...