Abstract
MotivationDNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes.ResultsHere we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub’s leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.
Highlights
DNA microarray analysis is an important tool in medicine and life sciences, because it measures simultaneously the expression levels of thousands of genes
For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes
We developed a novel three-class gene selection method for disease classification and prediction using mOPLS-DA models and S-plots to identify informative genes from microarray data
Summary
DNA microarray analysis is an important tool in medicine and life sciences, because it measures simultaneously the expression levels of thousands of genes. Microarray data typically consist of a relatively small sample size (usually several dozens) and a large number of genes (several thousands), most of which may be irrelevant, insignificant, or redundant for disease classification and prediction [15,16]. Many gene-selection approaches for cluster analysis have been proposed such as signal to noise ratio (S2N) [5], ANN [7], Kruskal-Wallis nonparametric one-way ANOVA (KW) [17], ratio of genes between-categories to within-category sums of squares (BW) [18], nonparametric test [19], t-test [20,21], genetic algorithm (GA), and k-nearest neighbor (GA/KNN) [22]. Many approaches were aimed to deal with two-class gene selection problems, and only a few studies involved multiclass gene selection (three classes or more) and classification for cluster analysis
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