The experimental data were presented as mean ± SD. Statistical significance was assessed by ANOVA test and paired-sample t-test was adopted to test normal distribution by SPSS v18.0 (SPSS, Inc., Chicago, IL, USA). In all experiments, confidence level was set at 95% to determine the significance of difference (p < 0.05).
Pearson correlation coefficients were calculated with SPSS v18.0 (IBM SPSS, USA). The correlation heat-map was built and optimized with HemI software (Deng et al., 2014 (link)). The correlation coefficient is always between −1 and +1. The closer the correlation is to ±1, the closer to a perfect linear relationship.
The results of a PCA are usually discussed in terms of component scores. Consider a data matrix, X, where each of the n rows represents different samples, and each of the p-columns gives the results tested factors. A set of p-dimensional vectors of loadings (αij) map each row vector (xi) of X to a new vector of the principal component scores (Fj), given by Fj = α1jx1+α2jx2+…+αijxi, for i = 1, 2, …, n, j = 1, 2, …, m. The full principal components score (F) decomposition of X can therefore be given as F = ρ1F1+ρ2F2+…ρjFj, where ρj is the jth eigenvector of Fj (Abdi and Williams, 2010 (link)).
Pearson correlation coefficients were calculated with SPSS v18.0 (IBM SPSS, USA). The correlation heat-map was built and optimized with HemI software (Deng et al., 2014 (link)). The correlation coefficient is always between −1 and +1. The closer the correlation is to ±1, the closer to a perfect linear relationship.
The results of a PCA are usually discussed in terms of component scores. Consider a data matrix, X, where each of the n rows represents different samples, and each of the p-columns gives the results tested factors. A set of p-dimensional vectors of loadings (αij) map each row vector (xi) of X to a new vector of the principal component scores (Fj), given by Fj = α1jx1+α2jx2+…+αijxi, for i = 1, 2, …, n, j = 1, 2, …, m. The full principal components score (F) decomposition of X can therefore be given as F = ρ1F1+ρ2F2+…ρjFj, where ρj is the jth eigenvector of Fj (Abdi and Williams, 2010 (link)).
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