PCA theoretical question for applied case
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Gero • 0
@gero
Last seen 13 hours ago
Spain

I think I am misunderstanding this mathematical concept. I have performed a PCA for microarray data; these are my whole results:

PCA = princomp(data.bgcorrect.norm$E)
PCA$sdev

 Comp.1    Comp.2    Comp.3    Comp.4    Comp.5    Comp.6    Comp.7    Comp.8    Comp.9   Comp.10 
9.7292845 1.3641271 0.9554555 0.8506955 0.7577389 0.6642390 0.6321169 0.5602993 0.5318998 0.4425422


PCA$loadings

            Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
Sample.1     0.319  0.169  0.213  0.402  0.379                0.108         0.710 
Sample.2     0.312  0.493         0.266 -0.662 -0.221  0.259 -0.177               
Sample.3     0.319  0.165  0.118  0.321  0.446  0.105  0.200  0.158  0.102 -0.684 
Sample.4     0.315  0.423 -0.230 -0.302        -0.203 -0.628  0.352 -0.113        
Sample.5     0.316  0.219        -0.669  0.215  0.168  0.408 -0.330  0.184  0.107 
Control.6    0.317 -0.319 -0.419  0.197 -0.131        -0.226 -0.132  0.704        
Control.7    0.319 -0.222 -0.116  0.145  0.144        -0.268 -0.656 -0.535        
Control.8    0.313 -0.201  0.754 -0.203 -0.267  0.279 -0.289         0.122        
Control.9    0.314 -0.466        -0.161        -0.723  0.265  0.223               
Control.10   0.317 -0.257 -0.333        -0.242  0.519  0.230  0.449 -0.369    


                Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
SS loadings       1.0    1.0    1.0    1.0    1.0    1.0    1.0    1.0    1.0     1.0
Proportion Var    0.1    0.1    0.1    0.1    0.1    0.1    0.1    0.1    0.1     0.1
Cumulative Var    0.1    0.2    0.3    0.4    0.5    0.6    0.7    0.8    0.9     1.0

As expected the fisrt component (Comp.1) shows greater sdv but why does not imply greater difference between the samples (PCA$loadings) than for example Comp.2? Why are some of them 'blank' for other components?

pcaMethods • 58 views
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