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Several biplot methods applied to gene expression data

Authors
Park, MiraLee, Jae WonLeec, Jung BokSong, Seuck Heun
Issue Date
1-2월-2008
Publisher
ELSEVIER SCIENCE BV
Keywords
gene expression data; biplot; supplementary data; principal component analysis; factor analysis; correspondence analysis; multidimensional scaling
Citation
JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.138, no.2, pp.500 - 515
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume
138
Number
2
Start Page
500
End Page
515
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/124098
DOI
10.1016/j.jspi.2007.06.019
ISSN
0378-3758
Abstract
DNA microarray experiments result in enormous amount of data, which need careful interpretation. Biplot approaches show simultaneous display of genes and samples in low-dimensional graphs and thus can be used to represent the relationships between genes and samples. There are several different types of biplots, and these methods need to be evaluated because each plot provides different result. In this paper, we review several variants of biplot methods such as principal component analysis biplot. factor analysis biplot, multidimensional scaling biplot and correspondence analysis biplot. We investigate the properties of these methods and compare their performances by analyzing various types of well-known gene expression data. We also suggest the supplementary data method as a tool for (i) classifying the previously unknown sample/gene to existing class, (ii) analyzing mixture data and (iii) presenting illustrative variables, etc. The usefulness of this approach for interpreting microarray data is demonstrated. (C) 2007 Elsevier B.V. All rights reserved.
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정경대학 (통계학과)
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