2 citations · 2 across the 1 of their papers we have counts for
2 papers
stat.ME2022★ 2 cited
From differential abundance to mtGWAS: accurate and scalable methodology for metabolomics data with non-ignorable missing observations and latent factors
Shangshu Zhao, Kedir Turi, Tina Hartert +5
Metabolomics is the high-throughput study of small molecule metabolites. Besides offering novel biological insights, these data contain unique statistical challenges, the most glar…
stat.ME2019
Estimation and inference in metabolomics with non-random missing data and latent factors
Chris McKennan, Carole Ober, Dan Nicolae
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 we…