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High throughput metabolomics data are fraught with both non-ignorable missingobservations and unobserved factors that influence a metabolite's measuredconcentration, and it is well known that ignoring either of these complicationscan compromise estimators. However, current methods to analyze these data canonly account for the missing data or unobserved factors, but not both. Wetherefore developed MetabMiss, a statistically rigorous method to account forboth non-random missing data and latent factors in high throughput metabolomicsdata. Our methodology does not require the practitioner specify a probabilitymodel for the missing data, and makes investigating the relationship betweenthe metabolome and tens, or even hundreds, of phenotypes computationallytractable. We demonstrate the fidelity of MetabMiss's estimates using bothsimulated and real metabolomics data. An R package that implements our methodis available from https://github.com/chrismckennan/MetabMiss.

Estimation and inference in metabolomics with non-random missing data and latent factors