I have 3 biofluids for which a group of fatty acids (almost the same in each biological matrice) has been measured, and i would like to use a reduction dimension method taking into account that variables are coming from 3 differents blocks (=biofluids). I already performed a simple PCA but i am trying to find a method that is more appropriate considering the 3 blocks aspect. I found that mbpca package (see mbpca Rdocumentation : https://www.rdocumentation.org/packages/mogsa/versions/1.6.4/topics/mbpca) is adapted for this kind of problematic proposing three different methods : consensus PCA (CPCA), generalized CCA (GCCA) or multiple co-inertia analsyis (MCIA). I read the article entitled "A framework for sequential multiblock component methods" https://doi.org/10.1002/cem.811 but i am not able to fully understand the differences between methods and to find which one is suitable for my case.
I would be grateful if someone could clarify this to me.

I tried the function mbpca as shown above but i have an error. I think it's because my dataframe is not appropriate.
In the Rdocumentation it is said that we shoud implement : A list of matrix or data.frame , where rows are variables and columns are samples. The columns among the matrices need to be match but the variables do not need to be.
In my case, for now, i have a matrice with the numeric values of each individual in rows and the variables in columns: each fatty acids from each biofluid is named differently (see below).
Should i have to swap rows and colomns ? or is there something else to do ?

i tried to transpose my matrice but the function still doesn't work : i have the error message : Error in svd.sol(tab) : infinite or missing values in 'x'.