Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.

ABSTRACT:

Introduction

Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment-even from the same cell lysate.

Objectives

To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets.

Methods

We analyzed metabolomics,

SUBMITTER: Tseng CY 

PROVIDER: S-EPMC12214035 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature