Table 3.

PCA results. (A) Decomposition of the correlation matrix expressed as eigenvalues, difference between adjacent eigenvalues, proportion of variance explained by each component and cumulative variance. (B) Component pattern (loadings). Each loading corresponds to the correlation coefficient between original variables (samples) and corresponding component.

(A)
Component	Eigenvalue	Difference	Proportion	Cumulative
1	9.874	9.832	0.987	0.987
2	0.042	0.017	0.004	0.991
3	0.025	0.010	0.003	0.994
4	0.015	0.003	0.002	0.996
(B)
Sample	PC1	PC2	PC3	PC4
PKH26+1	−0.317	0.156	−0.272	0.336
PKH26+2	−0.317	0.065	−0.151	0.255
PKH26+3	−0.317	0.100	−0.221	0.166
PKH26+4	−0.316	−0.038	0.694	0.478
PKH26+5	−0.314	−0.799	−0.040	0.087
PKH26−1	−0.316	0.347	0.353	−0.396
PKH26−2	−0.317	0.143	−0.363	−0.013
PKH26−3	−0.317	0.121	−0.262	−0.157
PKH26−4	−0.317	0.233	0.208	−0.163
PKH26−5	−0.316	−0.336	0.057	−0.591