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http://hdl.handle.net/1942/426
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DC Field | Value | Language |
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dc.contributor.author | Wouters, Luc | - |
dc.contributor.author | Gohlmann, Hinrich W. | - |
dc.contributor.author | BIJNENS, Luc | - |
dc.contributor.author | Kass, Stefan U. | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | Lewi, Paul J. | - |
dc.date.accessioned | 2004-10-29T09:20:29Z | - |
dc.date.available | 2004-10-29T09:20:29Z | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | Biometrics, 59(4). p. 1131-1139 | - |
dc.identifier.issn | 0006-341X | - |
dc.identifier.uri | http://hdl.handle.net/1942/426 | - |
dc.description.abstract | This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized. | - |
dc.description.sponsorship | We gratefully acknowledge support from the BelgianIUAP/PAI network “Statistical Techniques and Modeling forComplex Substantive Questions with Complex Data.” | - |
dc.format.extent | 1086172 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | BLACKWELL PUBLISHING LTD | - |
dc.subject | Bioinformatics,Genetic data analysis | - |
dc.subject | Multivariate data | - |
dc.subject.other | bioinformatics; biplot; correspondence factor analysis; data mining; data visualization; gene expression data; microarray data; multivariate exploratory data analysis; principal component analysis; spectral map analysis | - |
dc.title | Graphical exploration of gene expression data: a comparative study of three multivariate methods | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 1139 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1131 | - |
dc.identifier.volume | 59 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1111/j.0006-341X.2003.00130.x | - |
dc.identifier.isi | 000187501100044 | - |
item.validation | ecoom 2005 | - |
item.accessRights | Open Access | - |
item.fullcitation | Wouters, Luc; Gohlmann, Hinrich W.; BIJNENS, Luc; Kass, Stefan U.; MOLENBERGHS, Geert & Lewi, Paul J. (2003) Graphical exploration of gene expression data: a comparative study of three multivariate methods. In: Biometrics, 59(4). p. 1131-1139. | - |
item.fulltext | With Fulltext | - |
item.contributor | Wouters, Luc | - |
item.contributor | Gohlmann, Hinrich W. | - |
item.contributor | BIJNENS, Luc | - |
item.contributor | Kass, Stefan U. | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | Lewi, Paul J. | - |
crisitem.journal.issn | 0006-341X | - |
crisitem.journal.eissn | 1541-0420 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
molg13.pdf | Peer-reviewed author version | 1.06 MB | Adobe PDF | View/Open |
Wouters_et_al-2003-Biometrics.pdf Restricted Access | Published version | 1.04 MB | Adobe PDF | View/Open Request a copy |
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