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Figure 1 | BMC Genomics

Figure 1

From: Coevolution between simple sequence repeats (SSRs) and virus genome size

Figure 1

Geometric meaning of PCA explained by using bivariate normally distributed variables. Scatters of sample are distributed in the shape of ellipse roughly, then orthogonally rotate the original plane rectangular coordinates composed of X 1 and X 2 with an angle θ. By now, two original correlated variables(X 1 , X 2 )were transformed into two integrated and uncorrelated variables (Y1,Y 2 ). Because the variance of the original variables is greater in Y 1 axis than in Y 2 axis, so the minimum of information will be lost if integrated variable Y 1 is used for replacing all original variables. Hence,Y 1 is defined as the first principal component; in contrast, variance of variables is smaller in Y 2 axis, and it can explain minor information relative to Y 1 , soY 2 is called the second principal component.

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