| Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares | |
| Department | 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 |
Sun XM(孙小明)1,2 ; Yu, Xiao-Ping4; Liu Y(刘芸)1,2,3; Xu, Lu4; Di DL(邸多隆)1,2; Di DL(邸多隆)
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| 2012 | |
| Source Publication | Chemometrics and Intelligent Laboratory Systems
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| ISSN | 0169-7439 |
| Volume | 115Pages:37-43 |
| Abstract | 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. |
| Keyword | Metabonomics Biomarkers Discriminant Partial Least Squares Bootstrap Uninformative Variable Elimination |
| Subject Area | 分析化学与药物化学 |
| DOI | 10.1016/j.chemolab.2012.04.006 |
| Funding Organization | 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) |
| Indexed By | SCI |
| If | 2.291 |
| Language | 英语 |
| Funding Project | 药物工艺标准研究组 |
| compositor | 第一作者单位 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.licp.cn/handle/362003/20256 |
| Collection | 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 |
| Corresponding Author | Di DL(邸多隆) |
| Affiliation | 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 |
| Recommended Citation 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. |
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| 1-s2.0-S016974391200（723KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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