# How to interpret PCA output statistically and biologically?

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"))


Here is my PCA output of differnt KO genotypes); how can I explain/interpret the PCA output statistically and if possible biologically: esp, how this colors /contrib explain

• Oncidium, did you check it with the wikipedia page on pca or with the help page of the function? Also what kind of data have you used as input (this would help with the biological intepretation)? – llrs Jul 22 '18 at 15:56
• @Llopis, This PCA is from targeted proteomics data, sorry I have tagges rna-seq, it should be proteomics. – Dendrobium Jul 23 '18 at 10:25

Assuming you had used the vst() function in DESeq2 (I note you've tagged your post with RNAseq, so perhaps you're using DESeq2) then something like that above would be a reasonable description.