PMC:2644708 / 29962-33574 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/2644708","sourcedb":"PMC","sourceid":"2644708","source_url":"http://www.ncbi.nlm.nih.gov/pmc/2644708","text":"GRSN corrects non-linear distortions in representative microarray datasets\nWe have investigated the application of GRSN on a wide variety of microarray datasets including clinical sample datasets, cell culture datasets with various treatment modalities, and genetic mouse model datasets [18-21] [see Additional file 1]. Two examples are shown in Figure 5A with RMA pre-processing. 1) A mouse model (MKM dataset) of carcinogenesis in cultured clonal keratinocytes [22,23]. Samples were run on the Affymetrix® MOE430A GeneChip®. This dataset represents cell culture based experiments with minimal biological variance between replicate samples. 2) A study of limb development in a mouse model (SS dataset) courtesy of Dr. Scott Stadler at OHSU [24]. Samples were run on the Affymetrix® MOE430A GeneChip®. This study compares mutant vs. wild type mice with three female and three male replicates for each condition. In both cases we see a reduction in the systematic intensity-dependent artifacts observed in these samples with application of GRSN (Fig. 5A, right-hand column).\nFigure 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. When datasets are analyzed for Fold Change between two experimental conditions where each gene's average FC between conditions is plotted versus its average expression for both conditions on M vs. A plots, we also often see non-linear skewing in the data even after averaging replicate samples and regardless of the pre-processing method. This is again likely resulting from the systematic, intensity-dependent artifacts which have no biological significance and it appears at least in some cases to be exacerbated in datasets containing unbalanced numbers of up or down regulated genes. For example, Fig. 5B shows M vs. A plots of the GB dataset comparing 14 Fanconi Anemia samples to 11 normal bone marrow samples before and after applying GRSN. In this example, non-linear distortions are seen without GRSN correction when MAS 5.0 pre-processing is used (top row, left panel), as well as when RMA pre-processing (middle row, left panel), and dChip pre-processing (bottom row, left panel) are used. However, applying GRSN substantially reduces this skew in all cases (Fig. 5B, right-hand panels). Thus, in some datasets, FC assessments can be affected by non-linear artifacts even when averaging multiple, replicate samples and regardless of the probe set summary method used. For each summary method shown, GRSN effectively reduces this skew. The same results are seen with additional datasets [see Additional file 2].","divisions":[{"label":"Title","span":{"begin":0,"end":74}},{"label":"Figure 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