Skip to main content
Figure 5 | BMC Bioinformatics

Figure 5

From: Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data

Figure 5

GRSN corrects non-linear artifacts in representative microarray datasets. A. GRSN applied to two different microarray datasets. First row – late stage sample L3 from the MKM dataset. Second row – mutant Male sample MutM2 from the SS dataset. Columns 1–3 demonstrate the effect of GRSN on the selected samples as described in figure 2 above. The RMA probe set summary method was used in each. B. GRSN can reduce systematic non-linear artifacts which can affect fold change analysis regardless of pre-processing method. M vs. A plots showing fold change as a function of mean value and plotted on log base 2 scale. Both fold change and mean are calculated using multiple replicates, 14 FA samples and 11 Normal samples from the GB dataset (not just comparing two samples). A lowess smoothed curve is displayed to show the trend of the scatter plots. Three different summary methods are shown: Top row – MAS 5.0, Middle row – RMA, and Bottom row – dChip®. The results in the left column are without GRSN applied and the effect of applying GRSN to each of the respective methods is shown in the right column.

Back to article page