Linear Mixed Model Approach to Protein Significance Analysis

Authors: Jongsoo Jun, Taesung Park

Abstract: Discovering protein biomarkers is one of the important issues in biomedical researches. The enzymelinked immunosorbent assay (ELISA) is one of the traditional techniques for protein quantitation. Recently, the multiple reaction monitoring (MRM) mass spectrometry has been proposed as a new method for protein quantification and has been popular as an alternative to ELISA. However, not many analysis methods are available yet to analyse MRM data. Linear mixed models (LMMs) are effective in analysing MRM data. MSstats is one of the most widely used tools for MRM data analysis which is based on the LMMs. MSstats is well implemented on Skyline program and R programming language. However, LMMs often provide various significance results depending on model specification. Thus, sometimes it would be difficult to specify a right LMM for the analysis of MRM data. In this paper, we systematically investigated the effect of model specification on significance of proteins through simulation studies. Our results provide a practical guideline of using LMMs for MRM data analysis.

Design, Construction, Maintenance, ISSN / E-ISSN: 2944-912X / 2732-9984, Volume 2, 2022, Art. #1

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Jongsoo Jun, Taesung Park, "Linear Mixed Model Approach to Protein Significance Analysis," Design, Construction, Maintenance, vol. 2, pp. 1-7, 2022
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