Bioinformatics tools for cancer metabolomics

被引：84

作者：

Blekherman, Grigoriy ^{[2
]}

Laubenbacher, Reinhard ^{[2
,3
]}

Cortes, Diego F. ^{[2
]}

Mendes, Pedro ^{[2
,3
,4
,5
]}

Torti, Frank M. ^{[3
,6
]}

Akman, Steven ^{[3
,6
]}

Torti, Suzy V. ^{[3
,7
]}

Shulaev, Vladimir ^{[1
,2
,3
]}

机构：

[1] Univ N Texas, Dept Biol Sci, Coll Arts & Sci, Denton, TX 76203 USA

[2] Virginia Bioinformat Inst, Blacksburg, VA 24061 USA

[3] Wake Forest Univ, Ctr Comprehens Canc, Sch Med, Winston Salem, NC 27157 USA

[4] Univ Manchester, Sch Comp Sci, Manchester M1 7DN, Lancs, England

[5] Univ Manchester, Manchester Ctr Integrat Syst Biol, Manchester M1 7DN, Lancs, England

[6] Wake Forest Univ, Dept Canc Biol, Sch Med, Winston Salem, NC 27157 USA

[7] Wake Forest Univ, Dept Biochem, Sch Med, Winston Salem, NC 27157 USA

来源：

METABOLOMICS | 2011年 / 7卷 / 03期

基金：

美国国家卫生研究院;

关键词：

Metabolomics; Cancer; Metabolite profiling; NMR; Mass spectrometry; Bioinformatics; MASS-SPECTROMETRY DATA; SEQUENTIAL PAIRED COVARIANCE; LIQUID-CHROMATOGRAPHY; PATTERN-RECOGNITION; DIFFERENTIAL ANALYSIS; QUANTITATIVE-ANALYSIS; H-1-NMR SPECTROSCOPY; PLANT METABOLOMICS; GAS-CHROMATOGRAPHY; NMR-SPECTROSCOPY;

D O I：

10.1007/s11306-010-0270-3

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

引用

页码：329 / 343

页数：15

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