How to interpret PCA output statistically and biologically

Question

[WUSCHEL]

How can I interpret the PCA results statistically for biological data?

I have used FactoMineR and factoextra libraries for PCA

Scripts used:

library(FactoMineR)

res.PCA = PCA(df, scale.unit=TRUE, ncp=4, graph=F )
par(mfrow=c(1,2))
plot.PCA(res.PCA, axes=c(1, 2), choix="ind")
plot.PCA(res.PCA, axes=c(1, 2), choix="var")
dimdesc(res.PCA, axes=c(1,2))

library("factoextra")
fviz_pca_var(res.PCA, arrowsize = 1, labelsize = 3, repel = TRUE, col.var = "contrib", gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"))

output of differnt KO genotypes) : esp, how this colors /contrib explain

[JC]

Those are field sports ;) But what are you plotting is the contribution per dimension (that's why is an arrow or vector), for example, the values in Shot.put are correlated with an increase in Dimension 1, or 110m.hurdle is inversely correlated to the same axis. Remember that a PCA is a projection, so dimensions can and cannot be something biological related.

Further reading:

• http://setosa.io/ev/principal-component-analysis/
• https://medium.com/@aptrishu/understanding-principle-component-analysis-e32be0253ef0
• https://www.nature.com/articles/nmeth.4346

[WUSCHEL]

thanks, JC, I've upload original plot :)