Quantile normalization for combining gene-expression datasets

Abstract

ABSTRACTOur research aimed to improve survival prediction by combining gene expression datasets, and to apply molecular signatures across different datasets. Many methods have previously been developed to remove unwanted variations among datasets and maintain the wanted factor variations. However, for inter-study validation (ISV) research, a whole dataset is set aside for testing, and the statuses of wanted factors are assumed unknown for the whole dataset; thus, regression cannot be used to determine the unwanted variations for this dataset. In this study, quantile normalization (QN) was utilized to remove the unwanted dataset variations, after which the adjusted datasets were used for classification. It was observed that the datasets formed by QN combination in the study of ISV had superior prediction performance compared to the datasets combined by other methods. Combining datasets using QN could improve the prediction performance for the study of ISV.