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

arXiv: https://arxiv.org/abs/1909.02644 · HTML: https://mlsanity.com/paper/1909.02644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [High-dimensional logistic regression with missing data: Imputation, regularization, and universality](https://mlsanity.com/paper/2410.01093/) (2024)
- [A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS](https://mlsanity.com/paper/2303.10221/) (2023)
- [Improving Diseases Predictions Utilizing External Bio-Banks](https://mlsanity.com/paper/2504.00036/) (2025)

## Abstract (arXiv)

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.