Systematic Evaluation of Scaling Methods for Gene Expression Data

Abstract

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 a variety of 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 differences in different studies on the analysis 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 application 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 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 different data sets. We also provide evidence that the two types of gene expression data, namely temporal and non-temporal, need different types of analyses in order to use them effectively for uncovering functional information.