Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares

CORC > 兰州化学物理研究所 > 中国科学院兰州化学物理研究所 > 中科院西北特色植物资源化学重点实验室/甘肃省天然药物重点实验室

	Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares
	Sun XM(孙小明)1,2 ; Yu, Xiao-Ping 4; Liu Y(刘芸)1,2,3 ; Xu, Lu 4; Di DL(邸多隆)1,2 ; Di DL(邸多隆)
刊名	Chemometrics and Intelligent Laboratory Systems
	2012
卷号	115 页码:37-43
关键词	Metabonomics Biomarkers Discriminant partial least squares Bootstrap Uninformative variable elimination
ISSN号	0169-7439
通讯作者	邸多隆
英文摘要	Interpretation and mining of complex metabonomic data depend heavily on proper use of chemometric methods. Due to the "small n" paradigm and the absence of sufficient information concerning distribution of data, the classical parametric methods based on known theoretical distributions are sometimes unsuitable or unreliable to treat such data. Therefore, nonparametric methods requiring no or very limited assumptions provide useful alternative tools in many practical applications. In this paper, a new discriminant partial least squares combined with bootstrap and uninformative variable elimination (DPLS-BS-UVE) method is proposed for biomarker discovery in metabonomics. The method was tested on two real chromatographic data sets containing plasma metabolic profilings for S180 and H22 tumor-bearing mice. A robust version of c(j) was used as the cutoff criterion. The results of biomarker discovery were compared with those obtained using variable importance in the projection (VIP) as well as BS. It is demonstrated that similar results are obtained using the three methods and DPLS-BS-UVE could provide easy interpretation of raw data. When the resampling unit increases to 500, the results were not significantly affected. In conclusion, DPLS-BS-UVE is a reliable alternative method for biomarker discovery, especially when the sample size is small.
学科主题	分析化学与药物化学
收录类别	SCI
资助信息	the "Hundred Talents Program" of Chinese Academy of Sciences (CAS) in 2007;the National Natural Science Foundation of China (NSFC No. 20775083);the National Public Welfare Industry Projects of China (No. 201210010);Hangzhou Programs for Agricultural Science and Technology Gevelopment (No. 20101032B28);the Key Scientfic and Technological Innovation Team Program of Zhejiang Province (No. 2010R50028)
语种	英语
WOS记录号	WOS:000304900000005
内容类型	期刊论文
源URL	[http://210.77.64.217/handle/362003/20256]
专题	兰州化学物理研究所_中科院西北特色植物资源化学重点实验室/甘肃省天然药物重点实验室
通讯作者	Di DL(邸多隆)
作者单位	1.Chinese Acad Sci, Lanzhou Inst Chem Phys, Key Lab Chem NW Plant Resources, Lanzhou 730000, Peoples R China 2.Chinese Acad Sci, Lanzhou Inst Chem Phys, Key Lab Nat Med Gansu Prov, Lanzhou 730000, Peoples R China 3.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China 4.China Jiliang Univ, Coll Life Sci, Zhejiang Prov Key Lab Biometrol & Inspect & Quara, Hangzhou 310018, Zhejiang, Peoples R China
推荐引用方式 GB/T 7714	Sun XM,Yu, Xiao-Ping,Liu Y,et al. Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares[J]. Chemometrics and Intelligent Laboratory Systems,2012,115:37-43.
APA	Sun XM,Yu, Xiao-Ping,Liu Y,Xu, Lu,Di DL,&邸多隆.(2012).Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares.Chemometrics and Intelligent Laboratory Systems,115,37-43.
MLA	Sun XM,et al."Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares".Chemometrics and Intelligent Laboratory Systems 115(2012):37-43.