(A) A PCCA projection of the batch-corrected expression matrix that shows that expression reflects population structure. While the individuals, labeled according to their population, do not cluster as clearly as with genotype data (Fig 1B), there is clear population structure in the PCCA projection of the batch-corrected expression data. (B) A leave-one-out cross-validation experiment showing that individuals are approximately projected to their populations of origin even when the projection matrix is learned without their expression or genotype data. The mean re-construction errors in (A) the left-in samples and (B) the held-out samples are similar and overlayed on top of the Figure. The first two canonical correlations are 0.963 and 0.766...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
Association studies using unrelated individuals have become the most popular design for mapping comp...
Population structure in genotype data has been extensively studied, and is revealed by looking at th...
(A) PCA of the expression matrix fails to reveal clustering by population, whereas (B) PCA of the ge...
Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of dataset...
<p>PCA was performed using pair-wise sample covariance matrix of 187 samples and applied to the geno...
Each symbol represents an individual. (a) Shows the population structure of investigated major regio...
<p>A, Comparison of PCA applied to the empirical data (left) and one selected simulation (right). Th...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>PCA projection of samples taken from a set of nine populations arranged in a lattice, each of whi...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>A. Correlation circle of the variables used for the PCA, projected on the first two components. B...
Genotype data, consisting large numbers of markers, is used as demographic and association studies t...
Accurate inference of genetic discontinuities between populations is an essential component of intra...
Population structure in genotype data has been extensively studied, and is revealed by looking at th...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
Association studies using unrelated individuals have become the most popular design for mapping comp...
Population structure in genotype data has been extensively studied, and is revealed by looking at th...
(A) PCA of the expression matrix fails to reveal clustering by population, whereas (B) PCA of the ge...
Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of dataset...
<p>PCA was performed using pair-wise sample covariance matrix of 187 samples and applied to the geno...
Each symbol represents an individual. (a) Shows the population structure of investigated major regio...
<p>A, Comparison of PCA applied to the empirical data (left) and one selected simulation (right). Th...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>PCA projection of samples taken from a set of nine populations arranged in a lattice, each of whi...
Principal components analysis, PCA, is a statistical method commonly used in population genetics to ...
<p>A. Correlation circle of the variables used for the PCA, projected on the first two components. B...
Genotype data, consisting large numbers of markers, is used as demographic and association studies t...
Accurate inference of genetic discontinuities between populations is an essential component of intra...
Population structure in genotype data has been extensively studied, and is revealed by looking at th...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
Association studies using unrelated individuals have become the most popular design for mapping comp...
Population structure in genotype data has been extensively studied, and is revealed by looking at th...