Cross-platform pathway activity transformation and classification of microarray data

Abstract

One of the most challenging problems in microarray study is to analyze microarray data from different platforms. This will improve the reliability of the study, as number of samples is larger and it can be applied for rare disease study, for which only a few microarray data have been published. As different microarray platforms cover different number of genes, so the integrative study of two different platforms needs to be able to deal with the missing value issue. Many works have been done for cross-platform microarray data utilization but none of them have focused on gene-set based microarray data classification. In this study, we applied the Bayesian-based method to reconstruct the expression level of the missing genes before transforming it to the gene-set activity. Two gene-set activity transformation methods; Negatively Correlated Feature Set (NCFS-i) and Analysis-of-Variance Feature Set (AFS), were used to evaluate the performance of this method using actual microarray datasets. The results show that the imputation of missing data can improve the classification performance of the cross-platform study.