Systematic evaluation of scaling methods for gene expression data

Even after an experimentally prepared gene expression data set has been pre-processed to account for variations in the microarray technology, there may be inconsistencies between the scales of measurements in different conditions. This may happen for reasons such as the accumulation of gene expression data prepared by different laboratories into a single data set. A variety of scaling and transformation methods have been used for addressing these scale inconsistencies in different studies on the analysis of gene expression data sets. However, a quantitative estimation of their relative performance has been lacking. In this paper, we report an extensive evaluation of scaling and transformation methods for their effectiveness with respect to the important problem of protein function prediction. We consider several such commonly used methods for gene expression data, such as z-score scaling, quantile normalization, diff transformation, and two new scaling methods, sigmoid and double sigmoid, that have not been used previously in this domain to the best of our knowledge. We show that the performance of these methods can vary significantly across data sets, but Dsigmoid scaling and z-score transformation generally perform well for the two types of gene expression data, namely temporal and non-temporal, respectively.