MOTIVATION: The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets. RESULTS: VizStruct is an interactive visualization technique that reduces multi-dimensional data to two dimensions using the complex-valued harmonics of the discrete Fourier transform (DFT). In the 3D VizStruct extension, the multi-dimensional SNP data vectors are reduced to three dimensions using a combination of the DFT and the Kullback-Leibler divergence. The performance of 3D VizStruct was challenged with several biologically relevant published datasets that included human Chromosome 21, the human lipoprotein lipase (LPL) gene locus and the multi-locus genotypes of coral populations. In every case, the 3D VizStruct mapping provided an intuitive visual description of the key characteristics of the underlying multi-dimensional genotype.

MOTIVATION: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.

Visualization enables us to find structures, features, patterns and relationship in a dataset by presenting the data in various graphical forms with possible interactions. Recent development of DNA microarray technology can be used to measure the expression levels of thousands of genes simultaneously. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analysis and visualize these fast growing data. In this paper, we present VizCluster, an interactive visualization approach to cluster analysis, and its application on microarray data. VizCluster combines the merits of both high dimensional scatter-plot and parallel coordinates. Integrated with useful features, it can give a simple, fast, intuitive and yet powerful view of the data set. VizCluster supports three major analyzing modes: cluster/class discovery, class prediction, and class assessment. Its primary applications are the classification of samples on microarray datasets. The experiments are based on gene expression data from a study of multiple sclerosis and leukemia patients.

DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Most research is focusing on interpretation of the meaning of the data. However, the majority of methods are supervised, with less attention having been paid to unsupervised approaches which are important when domain knowledge is incomplete or hard to obtain. In this paper we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach to the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.