### 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 `NA`s 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 `NA`s 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](http://dx.doi.org/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: https://stats.stackexchange.com/q/19216/4598
- 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.