4.2. Nonlinear Normalization
It has been observed that the changes of gene expression levels are nonlinear, especially for those highly expressed genes/miRNAs. Loess normalization is a popular normalization method for miRNA microarrays, which is based on robust local regression of the log ratios of the intensity measures from two arrays (or two differently-dyed samples from the same array) on the overall spot intensities. Variants of the loess normalization method has also been introduced so as to refine the linear scaling part to enhance its performances [48,49,50]. Quantile normalization is another commonly used nonlinear normalization method, which has been successfully migrated to miRNA array data analysis. The quantile normalization method is proposed under an assumption that there is an underlying common distribution of intensities of all miRNAs across arrays [51,52,53]. For miRNA arrays, due to the small total number of miRNAs and the overall low expression level, the ranks of the miRNAs could be greatly affected by the background noises, and hence, the performances of the quantile normalization method could be affected. However, quantile normalization is reported in the literature as one of the best performed normalization methods for miRNA data [37,40,41,45,46,47]. For the FlexmiR bead arrays, due to the small number of miRNAs profiled in the five pools (60, 64,64, 65 and 66), the quantile normalization is not suitable for intra-sample (among pools) normalization. Though, after the sub-profiles have been appropriately assembled, the quantile normalization method has relatively better performance than some other normalization methods [41].