Dear list I'd like to have your opinion about my case study. I'm analizing a dataset of 9 experiments and 15 variables with the aim to highlight the variables that can majorly explain the variance between the experiments. This is an example with only 3 rows and 5 variables var1 var2 var3 var4 var5 sample5 0,067 0,005 0,008 0,100 0,005 sample6 0,069 0,001 0,011 0,084 0,005 sample7 -7 -5 -1 34 4 My problem is that in some experiments (like in sample7) the measures related to my variables are measured as delta values (initial condition - final condition). In the other cases the variables are measured considering only the absolute values at my final condition. After PCA the model looks like strongly influenced by this difference (even if my data are centered to 0 and scaled to 1) because in the score plot I see with the first PC mainly the separation between experiments with positive and negative values and the second PC is not able to give to me further informations . In your opinion is there a way to compare these experiments measured in this different way? Alternatively do you think that the Dual Multiple Factor Analysis available with the package FactorMineR could be a better way to analyze these data? Thank you for any suggestion Guido [[alternative HTML version deleted]]