Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data

摘要

Class prediction is an important application of microarray gene expressiondata analysis. The high-dimensionality of microarray data, where number ofgenes (variables) is very large compared to the number of samples (obser-vations), makes the application of many prediction techniques (e.g., logisticregression, discriminant analysis) difficult. An efficient way to solve thisprob- lem is by using dimension reduction statistical techniques. Increasinglyused in psychology-related applications, Rasch model (RM) provides an appealingframework for handling high-dimensional microarray data. In this paper, westudy the potential of RM-based modeling in dimensionality reduction withbinarized microarray gene expression data and investigate its prediction ac-curacy in the context of class prediction using linear discriminant analysis.Two different publicly available microarray data sets are used to illustrate ageneral framework of the approach. Performance of the proposed method isassessed by re-randomization scheme using principal component analysis (PCA) asa benchmark method. Our results show that RM-based dimension reduction is aseffective as PCA-based dimension reduction. The method is general and can beapplied to the other high-dimensional data problems.