使用R語言為PCA散點圖加置信區間的方法，我知道的有三種，分别是使用ggplot2,ggord,ggfortify三個包去繪制。後面兩個R包是基于ggplot2的快捷返方法。

現在拿一組數據集為例，使用先R中的prcomp()基礎函數完成主成分分析

查看數據類型，每一列代表一個樣本，每一行代表一個基因（變量） > head(a,3) X1_untreated X2_untreated X3_untreated X4_untreated X1_Dex X2_Dex ENSG00000223972 -2.089725 -2.090478 -2.090475 -2.089265 -2.079351 -2.087724 ENSG00000227232 6.760110 6.892673 6.346646 6.739761 6.450597 6.749787 ENSG00000243485 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 X3_Dex X4_Dex ENSG00000223972 -2.091304 -2.089408 ENSG00000227232 6.623112 6.524621 ENSG00000243485 0.000000 0.000000 b <- t(a)#PCA分析需要将表達矩陣轉置 -----------------标準化處理-------------- R函數：scale(data, center=T/F, scale=T/F)或者scale(data) 參數：center (中心化)将數據減去均值，默認為T 參數：scale (标準化)在中心化後的數據基礎上再除以數據的标準差,默認值F,建議改為T c <- prcomp(b[ , which(apply(data, 2, var) != 0)], scale=TRUE) summary(c) 可以看到pc1方差的解釋率達23.05%，pc2的方差的解釋率達18.8%，等等 Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Standard deviation 86.1425 77.7838 71.9321 64.5281 59.7924 54.48867 53.27203 3.021e-12 Proportion of Variance 0.2305 0.1880 0.1607 0.1293 0.1111 0.09223 0.08816 0.000e 00 Cumulative Proportion 0.2305 0.4185 0.5792 0.7086 0.8196 0.91184 1.00000 1.000e 00 >

提取不同記錄的PC1-PC8數值，即點的橫縱坐标值 dt = c$x > head(dt,3) PC1 PC2 PC3 PC4 PC5 PC6 PC7 X1_untreated -70.89354 47.84633 -93.91127 3.457347 -32.08983 -72.516671 60.80422 X2_untreated -89.78212 14.29189 61.29232 -102.245406 -39.00539 59.352006 21.05424 X3_untreated -81.40448 -105.32841 -15.33420 19.990771 107.24132 9.986499 4.28529 PC8 X1_untreated 2.802701e-12 X2_untreated 2.715383e-12 X3_untreated 2.618101e-12 condition condition 1 control 2 control 3 control 4 control 5 treat 6 treat 7 treat 8 treat 将condition列添加到dt中 dt = cbind(dt,condition) > head(dt,3) PC1 PC2 PC3 PC4 PC5 PC6 PC7 X1_untreated -70.89354 47.84633 -93.91127 3.457347 -32.08983 -72.516671 60.80422 X2_untreated -89.78212 14.29189 61.29232 -102.245406 -39.00539 59.352006 21.05424 X3_untreated -81.40448 -105.32841 -15.33420 19.990771 107.24132 9.986499 4.28529 PC8 condition X1_untreated 2.802701e-12 control X2_untreated 2.715383e-12 control X3_untreated 2.618101e-12 control 生成坐标軸标題 summ = summary(c) xlab = paste0("PC1(",round(summ$importance[2,1]*100,2),"%)") ylab = paste0("PC2(",round(summ$importance[2,2]*100,2),"%)") 載入ggplot2包 library(ggplot2) ggplot(data = dt,aes(x=PC1,y= PC2,color=condition)) stat_ellipse(aes(fill=condition),type="norm",geom="polygon",alpha=0.2,color=NA) geom_point() labs(x=xlab,y=ylab,color="") guides(fill=F)

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