Normalization of LC-MS data using Gaussian process

The purpose of normalization of data generated by liquid chromatography coupled with mass spectrometry (LC-MS) is to reduce bias due to differences in sample collection, biomolecule extraction, and instrument variability. In this paper, we introduce a Gaussian process model for normalization based on analysis order of the samples. Specifically, we use measurement variabilities estimated using quality control (QC) samples to correct for bias caused by instrument drift. Maximum likelihood approach is used to find the optimal parameters for the fitted Gaussian process. The performance of the proposed normalization method is compared with other methods that use the analysis order information for normalization.