PCA分析采用降维的思想对样本关系进行低维平面的投影。
使用的程序是R语言的vegan、ade4包
输入
丰度文件：
例如
# a1 a2 a3 b1 b2 b3
var1 10 34 51 19 21 31
var2 22 21 41 89 78 79
var3 66 87 56 76 89 90
var4 18 37 46 55 54 63
var5 19 40 50 61 58 76
分组文件：
例如，
#sample group
a1 a
a2 a
a3 a
b1 b
b2 b
b3 b
结果
例如
Eigvals 5
38.9248 17.35772 12.70343 2.545609 0.1087814
Proportion explained 5
0.76356 0.15184 0.08133 0.00327 0.00001
Species 0 0
Site 6 5
a1 -53.3158253786436 20.1097825573183 6.18979333427741 -1.81295096183138 -0.0490949449829294
a2 -32.6758839166003 -7.56931372418045 -18.6846592025341 1.20352490590713 0.102330998001516
a3 -14.5188276424877 -27.9604243148421 14.2573281713209 0.628953820570339 -0.036421539975502
b1 34.3941955199972 11.121861820717 11.0922337952386 0.443500848548519 0.16070864585441
b2 26.5019102588601 10.2124503187444 -4.13336931468519 3.44039904982497 -0.129834272118011
b3 39.6144311588742 -5.91435665775718 -8.7213267836176 -3.90342766301959 -0.0476888867794898
Biplot 0 0
Site constraints 0 0
