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Analysis of significant protein abundance from multiple reaction-monitoring data
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...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311902/ https://www.ncbi.nlm.nih.gov/pubmed/30598095 http://dx.doi.org/10.1186/s12918-018-0656-9 |
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author | 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 |
author_facet | 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 |
author_sort | Jun, Jongsu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6311902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63119022019-01-07 Analysis of significant protein abundance from multiple reaction-monitoring data 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 BMC Syst Biol Research 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. BioMed Central 2018-12-31 /pmc/articles/PMC6311902/ /pubmed/30598095 http://dx.doi.org/10.1186/s12918-018-0656-9 Text en © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research 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 Analysis of significant protein abundance from multiple reaction-monitoring data |
title | Analysis of significant protein abundance from multiple reaction-monitoring data |
title_full | Analysis of significant protein abundance from multiple reaction-monitoring data |
title_fullStr | Analysis of significant protein abundance from multiple reaction-monitoring data |
title_full_unstemmed | Analysis of significant protein abundance from multiple reaction-monitoring data |
title_short | Analysis of significant protein abundance from multiple reaction-monitoring data |
title_sort | analysis of significant protein abundance from multiple reaction-monitoring data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311902/ https://www.ncbi.nlm.nih.gov/pubmed/30598095 http://dx.doi.org/10.1186/s12918-018-0656-9 |
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