9 PCA 分析[PCA analysis]

主成分分析（Principal component analysis，PCA) 是一种统计方法。通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量，转换后的这组变量叫主成分。 每个点代表一个样本。

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Each dot represents a sample.

Figure 9.1: PCA analysis: PC1 vs. PC2

PDF 文件 : cuffnorm_pca_pc1_vs_pc2.jpg.

Figure 9.2: PCA analysis: PC1 vs. PC3

PDF 文件 : cuffnorm_pca_pc1_vs_pc3.jpg.

Figure 9.3: PCA analysis: PC2 vs. PC3

PDF 文件 : cuffnorm_pca_pc2_vs_pc3.jpg.

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