Summary: Rotation Forest (RF) is an ensemble method that has shown effectiveness on microarray data set classification problems. RF works by generating sparse rotation matrixes of the input space, a method that creates accurate and diverse base classifiers. In its original formulation, elemental rotations were obtained by Principal Component Analysis (PCA). However, for microarray data sets, Independent Component Analysis (ICA) may be a better option. In this paper, an experimental study on ten microarray data sets has been performed. The study confirms that, except for a small number of attributes, Rotation Forest outperforms Bagging and Boosting on this domain. However, RF with ICA does not generally improve on RF with PCA.