title: Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.
abstract: BACKGROUND
  DNA 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.
  
  
  RESULTS
  Here 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.
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China ; Department of Electronic Science, School of Physics and Mechanical & Electrical Engineering, Xiamen University, Xiamen, China.
authors.forename: Mingxing
authors.initials: M
authors.lastname: Yang
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Xiumin
authors.initials: X
authors.lastname: Li
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Li
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Zhimin
authors.initials: Z
authors.lastname: Ou
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Liu
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Suhuan
authors.initials: S
authors.lastname: Liu
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China ; Department of Endocrinology and Diabetes, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Xuejun
authors.initials: X
authors.lastname: Li
authors.affiliation: Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China.
authors.forename: Shuyu
authors.initials: S
authors.lastname: Yang
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D016018:Least-Squares Analysis; D007938:Leukemia / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D025341:Principal Component Analysis; D015203:Reproducibility of Results