  Class prediction is an important application of microarray gene expression
data analysis. The high-dimensionality of microarray data, where number of
genes (variables) is very large compared to the number of samples (obser-
vations), makes the application of many prediction techniques (e.g., logistic
regression, discriminant analysis) difficult. An efficient way to solve this
prob- lem is by using dimension reduction statistical techniques. Increasingly
used in psychology-related applications, Rasch model (RM) provides an appealing
framework for handling high-dimensional microarray data. In this paper, we
study the potential of RM-based modeling in dimensionality reduction with
binarized 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 a
general framework of the approach. Performance of the proposed method is
assessed by re-randomization scheme using principal component analysis (PCA) as
a benchmark method. Our results show that RM-based dimension reduction is as
effective as PCA-based dimension reduction. The method is general and can be
applied to the other high-dimensional data problems.
