Several biplot methods applied to gene expression data

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.