Figure 2
From: Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data
GRSN corrects non-linear distortions apparent in different summary methods. M vs. A plots demonstrating the GRSN method applied to the MAS 5.0, RMA, and dChip® probe set summary methods. Column 1 shows M vs. A plots comparing one selected sample to the virtual reference sample created by taking the trimmed mean expression value of each probe set in that dataset. Column 2 shows the global rank-invariant set (GRiS) of 5,000 probe sets before GRSN normalization in blue and after normalization in red (note change in y-axis scale). The smoothed curve through the rank-invariant set is shown in green. This is the calibration curve used to normalize the selected sample. Column 3 shows all probe sets after GRSN normalization of the selected sample compared to the virtual reference sample. The sample shown is N3 from the GB dataset. The probe set summary methods used are (from top to bottom): MAS 5.0, RMA, and dChip®.