I'm new in biostatistics and I work on something about GWAS with plink.
I used plink 1.9 to do the PCA with the code as following:
plink --bfile data --pca
and get the .eigenval and .eigenvec file.
I think the results of eigenval file (as following)is wired, is that mean the amount of variance accounted for by the principle component? if it is, should I count the percentage variance explained by certain PC divided by total variance? But the percentage is too small.how many eigenvectors should I choose then?
20.0134
2.98845
2.32333
1.94295
1.93421
1.91117
1.88628
1.86544
1.85781
1.84763
1.76204
1.5532
1.3277
1.1808
1.14857
1.13482
1.13316
1.12439
1.1194
1.11312
Thanks for your help. But we should choose the top m pcs which percentage add to at least 85%, is that right? And my eigenval file is just the variance, not the percentage, when I get the percentage,it will be too small
That depends on what exactly you're trying to do with the PCs; I don't know what scenario the 85% rule you mention was intended for. But your "too small" comment still does not make sense. Go ahead and take the top 10-15 PCs instead of the top 3 if that's appropriate for the analysis you are performing; but there's nothing wrong with the output file itself.