Improved precision provided by CQN on comparisons across studies. (a) We show boxplots of the estimated log fold change between the 2 groups of 5 samples (the same 2 groups as in Figure 3) from the Montgomery data using standard RPKM, expression values normalized by TMM (trimmed median of M-values, the method proposed in ), the method proposed in , and CQN with and without quantile normalization. We show genes with length greater than 100 bp and average (across all samples) log2-RPKM greater or equal to 2. (b) We normalized the 29 samples assayed in both Montgomery and Cheung. For each gene, we computed the mean squared difference between the expression measure based on the Montgomery and the Cheung data. The boxplots show the distribution of these precision measures for the highly expressed genes, for each of the 4 choices of normalization: standard RPKM, TMM, the method proposed in , and CQN. We show genes with length greater than 100 bp and average (across all samples) log2-RPKM greater or equal to 2. (c) For the MicroArray Quality Control data, we obtained fold change estimates between UHR and brain based on RNA-Seq and microarrays. For RNA-seq, we used 2 samples. For the microarrays, we used a 5 versus 5 comparison. The microarray data were normalized using Robust Multiarray Analysis, and the RNA-seq data were normalized by CQN.