Rasch in Genetics

From the Elsevier website:

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

Authors: Andrej Kastrin and Borut Peterlin, Institute of Medical Genetics, University Medical Centre Ljubljana, Slovenia.

Journal: Expert Systems with Applications (online 2010)

Abstract: 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 (observations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this problem 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 accuracy 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.

A. Kastrin, B. Peterlin. (2010). Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data. Expert Systems with Applications, 37, 7, 5178-5185

Kastrin A., Peterlin B. (2010) Rasch in Genetics, asch Measurement Transactions, 2010, 23:4, 1242

Rasch Publications
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Rating Scale Analysis, Wright & Masters Introduction to Rasch Measurement, E. Smith & R. Smith Introduction to Many-Facet Rasch Measurement, Thomas Eckes Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. Statistical Analyses for Language Testers, Rita Green
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Journal of Applied Measurement Rasch models for measurement, David Andrich Constructing Measures, Mark Wilson Rasch Analysis in the Human Sciences, Boone, Stave, Yale
in Spanish: Análisis de Rasch para todos, Agustín Tristán Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez

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