Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data.

ABSTRACT:

Motivation

Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments allows profiling of thousands of known and unknown molecules in parallel. Inter-batch differences constitute a common and unresolved problem in untargeted metabolomics, and hinder the analysis of multi-batch studies or the intercomparison of experiments.

Results

We present a new method, Regularized Adversarial Learning Preserving Similarity (RALPS), for the normalization of multi-batch untargeted metabolomics data. RALPS builds on deep adversarial learning with a three-term loss function that mitigates batch eff

SUBMITTER: Dmitrenko A 

PROVIDER: S-EPMC9978579 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature