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Analysis of significant protein abundance from multiple reaction-monitoring data

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Authors
Jun, Jongsu; Gim, Jungsoo; Kim, Yongkang; Kim, Hyunsoo; Yu, Su Jong; Yeo, Injun; Park, Jiyoung; Yoo, Jeong-Ju; Cho, Young Youn; Lee, Dong Hyeon; Cho, Eun Ju; Lee, Jeong-Hoon; Kim, Yoon Jun; Lee, Seungyeoun; Yoon, Jung-Hwan; Kim, Youngsoo; Park, Taesung
Issue Date
2018-12-31
Publisher
BioMed Central
Citation
BMC Systems Biology, 12(Suppl 9):123
Keywords
Multiple reaction-monitoring (MRM)ProteinLogistic regression-based method for significance analysis of multiple reaction monitoring (LR-SAM)Hepatocellular carcinoma (HCC)Sorafenib response
Abstract
Background
Discovering 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).

Results
Through 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.

Conclusion
As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.
ISSN
1752-0509
Language
English
URI
http://hdl.handle.net/10371/147091
DOI
https://doi.org/10.1186/s12918-018-0656-9
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Journal Papers (저널논문_통계학과)
Graduate School of Public Health (보건대학원)Dept. of Public Health (보건학과)Journal Papers (저널논문_보건학과)
College of Medicine/School of Medicine (의과대학/대학원)Biomedical Engineering (의공학전공)Journal Papers (저널논문_의공학전공)
College of Medicine/School of Medicine (의과대학/대학원)Internal Medicine (내과학전공)Journal Papers (저널논문_내과학전공)
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
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