Abstract

Out of the thousands of metabolites in a given specimen, most metabolomics experiments measure only hundreds, with poor overlap across experimental platforms. Here, we describe Metabolite Imputation via Rank-Transformation and Harmonization (MIRTH), a method to impute unmeasured metabolite abundances by jointly modeling metabolite covariation across datasets which have heterogeneous coverage of metabolite features. MIRTH successfully recovers masked metabolite abundances both within single datasets and across multiple, independently-profiled datasets. MIRTH demonstrates that latent information about otherwise unmeasured metabolites is embedded within existing metabolomics data, and can be used to generate novel hypotheses and simplify existing metabolomic workflows.

Details

Title
MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization
Author
Freeman, Benjamin A; Jaro, Sophie; Park, Tricia; Keene, Sam; Tansey, Wesley; Reznik, Ed
Pages
1-25
Section
Method
Publication year
2022
Publication date
2022
Publisher
BioMed Central
ISSN
14747596
e-ISSN
1474760X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2715494207
Copyright
© 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.