Missing values
As @gung says, a first step would be to go and find out why there are NAs:
concentrations are below LOD/LOQ/signal below critical value: ask for the uncensored values. At the very least make sure that in future data is not censored and instead you are provided with the critical values/LOD/LOQ in addition to the data.
Btw: known LOD and LOQ implies that a calibration is available.Obviously, if
NAs are not due to censoring but mean that for some reason measurement was not possible, you'll have to go on with missing values.Whether the
NAs come from (left) censoring or other reasons is important for dealing with them.Ivana Stanimirova from Katowice has done a lot of work about data analysis with missing values both completely at random (CAR) and/or due to censoring.
I. Stanimirova: Practical approaches to principal component analysis for simultaneously dealing with missing and censored elements in chemical data, Analytica Chimica Acta, 796 (2013) 27–37.
DOI: 10.1016/j.aca.2013.08.026
may be a good starting point.
Signals instead of concentrations and scaling
- are not very problematic:
different metabolites come at vastly varying concentrations as well, so you'd anyways need to think about scaling - if all signals are physically the same unit and don't vary in their order of magnitude, you may keep them as they are
- see also: Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?
- Sometimes it is possible to set up scaling according to some control group.
Study factors
ASCA (ANOVA Simultaneous Component Analysis), PCA-ANOVA and rMANOVA (regularized MANOVA) could be starting points.
