Analysis of significant protein abundance from multiple reaction-monitoring data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12-31

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

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. More... »

PAGES

123

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12918-018-0656-9

DOI

http://dx.doi.org/10.1186/s12918-018-0656-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111007808

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30598095


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23 schema: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.
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31 HCC data
32 LMM approach
33 LMM method
34 LR-SAM
35 LR-SAM approach
36 MRM data
37 MRM data analysis
38 MSstats
39 abundance
40 accurate quantitation
41 alternative
42 analysis
43 applications
44 approach
45 biomarkers
46 biomedical research
47 candidate proteins
48 carcinoma patients
49 cases
50 clinical response
51 contrast
52 correct LMM method
53 data
54 data analysis
55 effect
56 effect size
57 error
58 false positive errors
59 hepatocellular carcinoma patients
60 high false-positive error
61 identification
62 identification of proteins
63 important issue
64 inhibitor sorafenib
65 issues
66 kinase inhibitor sorafenib
67 linear mixed modeling
68 logistic regression-based method
69 mass spectrometry
70 method
71 mixed modeling
72 model specification
73 modeling
74 monitoring
75 most cases
76 multiple reaction monitoring
77 multiple reaction monitoring-mass spectrometry
78 multiple reaction-monitoring data
79 new logistic regression-based method
80 patients
81 peptides
82 popular alternative
83 protein
84 protein abundance
85 protein biomarkers
86 quantitation
87 random effects
88 reaction monitoring
89 reaction monitoring-mass spectrometry
90 reaction-monitoring data
91 regression-based methods
92 reliable protein biomarkers
93 research
94 response
95 results
96 same protein
97 significance
98 significance analysis
99 significance results
100 significant protein abundance
101 significant results
102 simulation study
103 size
104 sorafenib
105 specification
106 spectrometry
107 study
108 technique
109 tool
110 traditional techniques
111 treatment
112 type I
113 tyrosine kinase inhibitor sorafenib
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