I am working on a metabolomics data set of 81 samples x 407 variables with ~17% missing data. I would like to compare a number of imputation methods to see which is best for my data.
Is there a general rule for the order of pre-treating a data set? Should I impute first and normalize after or normalize first?
I have tried both ways with k-nearest-neighbor imputation and normalization to the median and compared the results using PCA and there are very few differences in the factor maps.
However when using Random Forest imputation the imputation error is much higher if I normalize the data first (normalized data NRMSE = 0.708, raw data NRMSE = 0.122).
My two main questions are:
Should imputation or normalization of data come first? and
Does the order depend on the imputation function used?