Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer

Jing Wang, Kim Anh Do, Sijin Wen, Spyros Tsavachidis, Timothy J. Mcdonnell, Christopher J. Logothetis, Kevin R. Coombes
2006 Cancer Informatics  
Motivation: Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method: We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. Results: We combined two prostate cancer microarray data sets, confirmed the
more » ... ateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%. Availability: Affymetrix U95Av2 array data are available at
doi:10.1177/117693510600200009 fatcat:a4kuzrjbpzailm6j4nm4isn3ii