Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets

doi:10.1186/1471-2105-13-24

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	Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets
	Yao, Fangzhou 1,2; Coquery, Jeff 1,3; Le Cao, Kim-Anh 1
刊名	BMC BIOINFORMATICS
	2012-02-03
卷号	13
ISSN号	1471-2105
DOI	10.1186/1471-2105-13-24
英文摘要	Background: A key question when analyzing high throughput data is whether the information provided by the measured biological entities (gene, metabolite expression for example) is related to the experimental conditions, or, rather, to some interfering signals, such as experimental bias or artefacts. Visualization tools are therefore useful to better understand the underlying structure of the data in a 'blind' (unsupervised) way. A well-established technique to do so is Principal Component Analysis (PCA). PCA is particularly powerful if the biological question is related to the highest variance. Independent Component Analysis (ICA) has been proposed as an alternative to PCA as it optimizes an independence condition to give more meaningful components. However, neither PCA nor ICA can overcome both the high dimensionality and noisy characteristics of biological data.
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
语种	英语
出版者	BIOMED CENTRAL LTD
WOS记录号	WOS:000301384500001
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/2789]
专题	上海财经大学
通讯作者	Le Cao, Kim-Anh
作者单位	1.Univ Queensland, Queensland Facil Adv Bioinformat, St Lucia, Qld 4072, Australia; 2.Shanghai Univ Finance & Econ, Shanghai, Peoples R China; 3.SupBiotech, F-94800 Villejuif, France
推荐引用方式 GB/T 7714	Yao, Fangzhou,Coquery, Jeff,Le Cao, Kim-Anh. Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets[J]. BMC BIOINFORMATICS,2012,13.
APA	Yao, Fangzhou,Coquery, Jeff,&Le Cao, Kim-Anh.(2012).Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets.BMC BIOINFORMATICS,13.
MLA	Yao, Fangzhou,et al."Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets".BMC BIOINFORMATICS 13(2012).