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Biplot methodology in exploratory analysis of microarray data
Author(s) -
GardnerLubbe S.,
le Roux N. J.,
Maunders H.,
Shah V.,
Patwardhan S.
Publication year - 2009
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10038
Subject(s) - biplot , principal component analysis , microarray analysis techniques , gene chip analysis , data mining , computer science , expression (computer science) , microarray databases , dna microarray , computational biology , biology , artificial intelligence , gene , genetics , gene expression , genotype , programming language
Although principal component analysis is widely used in the exploration of microarray data, the advantages of constructing a biplot as multivariate analog to a scatterplot is seldom exploited. This paper illustrates the benefits of using biplots with microarray data to (1) visually display both the treatments and genes of such extreme high‐dimensional data in a single plot, (2) relate the treatments to the underlying biological process through the use of biplot axes, and (3) to optimally separate classes and explore the differentially associated expression in genes. In this analysis, we have used gene expression measurements from human bronchial epithelial cells following exposure to whole cigarette smoke. Specifically, when exploring differences between treatments and differentially expressed genes, it is shown why the principal component biplot is not optimal and the analysis of distance biplot is introduced as an alternative to principal components. Copyright © 2009 Wiley Periodicals, Inc., Statistical Analysis and Data Mining 2: 135‐145, 2009