Analysis of significant protein abundance from multiple reaction-monitoring data |
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Authors: | Jongsu Jun Jungsoo Gim Yongkang Kim Hyunsoo Kim Su Jong Yu Injun Yeo Jiyoung Park Jeong-Ju Yoo Young Youn Cho Dong Hyeon Lee Eun Ju Cho Jeong-Hoon Lee Yoon Jun Kim Seungyeoun Lee Jung-Hwan Yoon Youngsoo Kim Taesung Park |
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Affiliation: | 1.Department of Statistics,Seoul National University,Seoul,South Korea;2.Graduate School of Public Health,Seoul National University,Seoul,South Korea;3.Department of Biomedical Engineering,Seoul National University College of Medicine,Seoul,South Korea;4.Institute of Medical and Biological Engineering, Medical Research Center,Seoul National University College of Medicine,Seoul,South Korea;5.Department of Internal Medicine and Liver Research Institute,Seoul National University,Seoul,South Korea;6.Department of Mathematics and Statistics,Sejong University,Seoul,South Korea;7.Interdisciplinary program in Bioinformatics,Seoul National University,Seoul,South Korea |
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Abstract: | BackgroundDiscovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM).ResultsThrough simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent.ConclusionAs exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did. |
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