# Selecting genes that contribute the most to the principal components [duplicate]

I have done a PCA analysis on genes expressed in cells under different stimulations, and retrieved the eigenvectors for a number of components.

My question is can I use these to determine which genes (or microarray probes) contribute to the principal components the most? Or is this an inappropriate use of the statistics?

If the answer is yes, is it the magnitude of the eigenvector's element or the sign ($+$ or $-$) of the element that is important?

Below is an example of eigenevectors output for a random selection of probes:

    probe     PC1            PC2            PC3           PC4            PC5
7894603  -0.00437706    -0.011776456    0.0027118     0.002236574   -0.009450911
7953873   0.018982512    0.013157616   -0.050475872   0.006077795    0.000618833
7894231   0.00239363    -0.013645266    0.006057671  -0.007046099    0.006165455
7895608   0.004984847    0.009435022   -0.00122576    0.009295082   -0.003496827
7893924   0.004356782    0.002382756    0.002338561  -0.010994646   -0.015574907
8095680   0.02053544     0.089269843    0.008331465  -0.017682479    0.002557484
7893452   0.003530932   -0.000148751    0.001704203  -0.0001649     -0.00419296
7961026   0.018171076    0.011570336   -0.04739463    0.005997931    0.000268158
8099393  -0.007035288   -0.014426004    0.012909486  -0.024354002   -0.003115809
7895836   0.007058346   -0.002625799    0.001665055   0.009596538   -0.004969979
7895884   0.005918773   -0.001403533   -5.63E-06      0.000485206   -0.001941824
7931681   0.00101047     0.002444639   -0.003501791  -0.007781566    0.003279817
7896555  -0.001349106   -0.00271741    -0.002717482   0.002233903   -0.007859473
8117018  -0.00053159     0.004344694    0.001928193  -0.020047103    0.001272397