The data were normalized using quantile normalization (BOLSTAD http://www.ncbi.nlm.nih.gov/pubmed/12538238).nih.gov/pubmed/12538238
The data were normalized using quantile normalization, and expression measures were produced by fitting the RMA robust linear model.
The data were normalized using TMM normalization, implemented through the edgeR package in R (Robinson and Oshlack 2010).
The data were normalized using quantile normalization with the RMA algorithm for gene-level intensities and the ratio determined for each gene using Partek Genomics Suite (Partek Inc., St . Louis MO, USA).
The data were normalized using the default normalization method.
The data were normalized using the LOWESS normalization algorithm within JMP Genomics.
The data were normalized using Lowess print-tip normalization as described previously [ 19].
The data were normalized using subset-quantile within array normalization (SWAN) [ 52], and potential batch effects were removed using ComBat [ 53].
The data were normalized using robust multi-array average (RMA) normalization.
The data were normalized using per spot and per chip LOWESS normalization.
The proteomic data are presented as weighted averages of the ratios of the relevant peptides (ratio calculated with respect to the reference standard) as described, and the data were normalized using the applied bias with β-actin as a normalizing factor.
