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Updated: Jul 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Normalization regarding non-random missing values in high-throughput mass spectrometry data
Pei Wang1, Hua Tang, Heidi Zhang
1Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. pwang@fhcrc.org
Abstract:
We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.
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