Fig. 2

When to use quantile normalization? Examples of gene expression data with targeted changes and global changes in distributions across groups. a Transformed read counts from n = 65 RNA-Seq samples from the Yoruba (YRI) population and colored by genotype based on the eQTL rs7639979: GG (blue), GA (green) and AA (red). As no global differences in distributions were detected, this suggests quantile normalization is appropriate, but not necessary as there is a low level of variation within and between groups. b Raw perfect match (PM) values from n = 45 arrays comparing the gene expression of alveolar macrophages from nonsmokers (green), smokers (red) and patients with asthma (blue). No global differences in distributions were detected, which indicates quantile normalization is appropriate, as it will remove any platform-based technical variability or batch effects within groups. c Raw PM values from n = 82 arrays comparing brain and liver tissue samples. The samples are colored by tissue (brain [red] and liver [green]), and the shades represent different Gene Expression Omnibus IDs. The global differences in distributions detected across brain and liver tissues indicate quantile normalization is not appropriate. Global changes caused by technical variation (e.g., batch effects across groups) will also be detected by quantro, but raw data alone cannot detect this difference