In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values

Abstract Considering as one of the major goals in quantitative proteomics, detection of the differentially expressed proteins (DEPs) plays an important role in biomarker selection and clinical diagnostics. There have been plenty of algorithms and tools focusing on DEP detection in proteomics researc...

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Autores principales:	Jinxia Wang, Liwei Li, Tao Chen, Jie Ma, Yunping Zhu, Jujuan Zhuang, Cheng Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/77def7792c2043d39463486147d19c8d
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spelling	oai:doaj.org-article:77def7792c2043d39463486147d19c8d2021-12-02T16:08:00ZIn-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values10.1038/s41598-017-03650-82045-2322https://doaj.org/article/77def7792c2043d39463486147d19c8d2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03650-8https://doaj.org/toc/2045-2322Abstract Considering as one of the major goals in quantitative proteomics, detection of the differentially expressed proteins (DEPs) plays an important role in biomarker selection and clinical diagnostics. There have been plenty of algorithms and tools focusing on DEP detection in proteomics research. However, due to the different application scopes of these methods, and various kinds of experiment designs, it is not very apparent about the best choice for large-scale proteomics data analyses. Moreover, given the fact that proteomics data usually contain high percentage of missing values (MVs), but few replicates, a systematic evaluation of the DEP detection methods combined with the MV imputation methods is essential and urgent. Here, we analyzed a total of four representative imputation methods and five DEP methods on different experimental and simulated datasets. The results showed that (i) MV imputation could not always improve the performances of DEP detection methods and the imputation effects differed in the missing value percentages; (ii) the DEP detection methods had different statistical powers affected by the percentage of MVs. Two statistical methods (i.e. the empirical Bayesian random censoring threshold model, and the significance analysis of microarray) performed better than the other evaluated methods in terms of accuracy and sensitivity.Jinxia WangLiwei LiTao ChenJie MaYunping ZhuJujuan ZhuangCheng ChangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinxia Wang Liwei Li Tao Chen Jie Ma Yunping Zhu Jujuan Zhuang Cheng Chang In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
description	Abstract Considering as one of the major goals in quantitative proteomics, detection of the differentially expressed proteins (DEPs) plays an important role in biomarker selection and clinical diagnostics. There have been plenty of algorithms and tools focusing on DEP detection in proteomics research. However, due to the different application scopes of these methods, and various kinds of experiment designs, it is not very apparent about the best choice for large-scale proteomics data analyses. Moreover, given the fact that proteomics data usually contain high percentage of missing values (MVs), but few replicates, a systematic evaluation of the DEP detection methods combined with the MV imputation methods is essential and urgent. Here, we analyzed a total of four representative imputation methods and five DEP methods on different experimental and simulated datasets. The results showed that (i) MV imputation could not always improve the performances of DEP detection methods and the imputation effects differed in the missing value percentages; (ii) the DEP detection methods had different statistical powers affected by the percentage of MVs. Two statistical methods (i.e. the empirical Bayesian random censoring threshold model, and the significance analysis of microarray) performed better than the other evaluated methods in terms of accuracy and sensitivity.
format	article
author	Jinxia Wang Liwei Li Tao Chen Jie Ma Yunping Zhu Jujuan Zhuang Cheng Chang
author_facet	Jinxia Wang Liwei Li Tao Chen Jie Ma Yunping Zhu Jujuan Zhuang Cheng Chang
author_sort	Jinxia Wang
title	In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
title_short	In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
title_full	In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
title_fullStr	In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
title_full_unstemmed	In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
title_sort	in-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/77def7792c2043d39463486147d19c8d
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In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values

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