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Analysis of significant protein abundance from multiple reaction-monitoring data
DC Field | Value | Language |
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dc.contributor.author | Jun, Jongsu | - |
dc.contributor.author | Gim, Jungsoo | - |
dc.contributor.author | Kim, Yongkang | - |
dc.contributor.author | Kim, Hyunsoo | - |
dc.contributor.author | Yu, Su Jong | - |
dc.contributor.author | Yeo, Injun | - |
dc.contributor.author | Park, Jiyoung | - |
dc.contributor.author | Yoo, Jeong-Ju | - |
dc.contributor.author | Cho, Young Youn | - |
dc.contributor.author | Lee, Dong Hyeon | - |
dc.contributor.author | Cho, Eun Ju | - |
dc.contributor.author | Lee, Jeong-Hoon | - |
dc.contributor.author | Kim, Yoon Jun | - |
dc.contributor.author | Lee, Seungyeoun | - |
dc.contributor.author | Yoon, Jung-Hwan | - |
dc.contributor.author | Kim, Youngsoo | - |
dc.contributor.author | Park, Taesung | - |
dc.date.accessioned | 2019-03-13T08:48:04Z | - |
dc.date.available | 2019-03-13T17:52:17Z | - |
dc.date.issued | 2018-12-31 | - |
dc.identifier.citation | BMC Systems Biology, 12(Suppl 9):123 | ko_KR |
dc.identifier.issn | 1752-0509 | - |
dc.identifier.uri | https://hdl.handle.net/10371/147091 | - |
dc.description.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. | ko_KR |
dc.description.sponsorship | This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165). Publication of this article was sponsored by HI16C2037 grant. | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Multiple reaction-monitoring (MRM) | ko_KR |
dc.subject | Protein | ko_KR |
dc.subject | Logistic regression-based method for significance analysis of multiple reaction monitoring (LR-SAM) | ko_KR |
dc.subject | Hepatocellular carcinoma (HCC) | ko_KR |
dc.subject | Sorafenib response | ko_KR |
dc.title | Analysis of significant protein abundance from multiple reaction-monitoring data | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 전종수 | - |
dc.contributor.AlternativeAuthor | 김정수 | - |
dc.contributor.AlternativeAuthor | 김영강 | - |
dc.contributor.AlternativeAuthor | 김현수 | - |
dc.contributor.AlternativeAuthor | 유수종 | - |
dc.contributor.AlternativeAuthor | 박지영 | - |
dc.contributor.AlternativeAuthor | 유정주 | - |
dc.contributor.AlternativeAuthor | 조영윤 | - |
dc.contributor.AlternativeAuthor | 이동현 | - |
dc.contributor.AlternativeAuthor | 조은주 | - |
dc.contributor.AlternativeAuthor | 이정훈 | - |
dc.contributor.AlternativeAuthor | 김윤준 | - |
dc.contributor.AlternativeAuthor | 이승연 | - |
dc.contributor.AlternativeAuthor | 윤정환 | - |
dc.contributor.AlternativeAuthor | 김영수 | - |
dc.contributor.AlternativeAuthor | 박태성 | - |
dc.identifier.doi | 10.1186/s12918-018-0656-9 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2019-01-06T04:14:40Z | - |
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