  High throughput metabolomics data are fraught with both non-ignorable missing
observations and unobserved factors that influence a metabolite's measured
concentration, and it is well known that ignoring either of these complications
can compromise estimators. However, current methods to analyze these data can
only account for the missing data or unobserved factors, but not both. We
therefore developed MetabMiss, a statistically rigorous method to account for
both non-random missing data and latent factors in high throughput metabolomics
data. Our methodology does not require the practitioner specify a probability
model for the missing data, and makes investigating the relationship between
the metabolome and tens, or even hundreds, of phenotypes computationally
tractable. We demonstrate the fidelity of MetabMiss's estimates using both
simulated and real metabolomics data. An R package that implements our method
is available from https://github.com/chrismckennan/MetabMiss.
