PMC:2644708 / 33576-36536 JSONTXT

{"project":"NEUROSES","denotations":[{"id":"T723","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T724","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T725","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T726","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T727","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T733","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T734","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T735","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T736","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T737","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T738","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T739","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T740","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T741","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T742","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T743","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T688","span":{"begin":1619,"end":1624},"obj":"PATO_0000014"},{"id":"T689","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T690","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T691","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T692","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T693","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T694","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T695","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T696","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T697","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T698","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T699","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T700","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T701","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T702","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T703","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T704","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T705","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T706","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T707","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T708","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T709","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T710","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T711","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T712","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T713","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T714","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T715","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T716","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T717","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T718","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T719","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T720","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T721","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T722","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"},{"id":"T728","span":{"begin":2646,"end":2652},"obj":"PATO_0000461"},{"id":"T729","span":{"begin":2066,"end":2071},"obj":"PATO_0000014"},{"id":"T730","span":{"begin":2450,"end":2455},"obj":"PATO_0000014"},{"id":"T731","span":{"begin":1728,"end":1738},"obj":"PATO_0000076"},{"id":"T732","span":{"begin":1925,"end":1931},"obj":"PATO_0000461"}],"text":"Reduction of systematic variation by GRSN\nThe goal of GRSN is to reduce systematic non-linear variation in microarray datasets. GRSN is very successful at this task as demonstrated with simulated data. However, there is also random variation in any microarray dataset and this random variation tends to be larger than the systematic variation addressed by GRSN. As a result, applying GRSN will not reduce the variation of all genes and the variation of some genes will actually increase due to the random nature of the non-systematic variation. Still, in most cases, GRSN will reduce the average variation among replicates as shown in Fig. 6. The main benefit seen from this reduction in average variance is in the genes with relatively small random and biological variations. These genes are at the largest risk of becoming false positives due to systematic non-linear artifacts. An example of this is seen in the SS dataset (Fig. 7C).\nFigure 6 GRSN reduces average variance in datasets. Lowess curves are plotted summarizing the variance (for log base 2 scaled data) of all genes among selected sets of replicate samples and whole datasets. The curves show the trend in the variance as a function of expression values. Dashed blue is RMA processed data and dashed red is RMA processed data with GRSN post processing. A. RS dataset showing variance reduction in Control samples, Myc samples, and control and Myc samples combined. B. SS dataset showing, WT samples, mutant samples, and WT and mutant samples combined.\nFigure 7 GRSN impacts gene discovery. Averaged fold change M vs. A plots as in Figure 5B, but with color coding added to show genes passing fold change and statistical thresholds for significant differential regulation between experimental conditions. Statistical thresholds reported in this figure are for FDR adjusted p-values from standard t-tests. A) GB data comparing 14 Fanconi Anemia samples to 11 Normal samples and plotted using values from RMA method alone (left panel) and using values from RMA with GRSN (right panel). Both plots are color coded to show genes found to be significantly changed (FC of at least 1.5 and FDR of no more than 0.05): genes found only when using RMA alone are in blue, genes found only when using RMA with GRSN are in red, and genes found in both cases are in yellow. The horizontal colored lines show the fold change cutoff applied to the respective summary and normalization methods. B-C) Color coding is modified so that blue genes are shown only in the left panel and red genes are shown only in the right panel. B) GSE6475 data comparing 6 AL (acne lesion) replicates to 6 AN (acne normal) replicates. Samples are plotted as in A. C) SS data comparing 6 mutant samples to 6 wild type (3 male and 3 female for each condition). No FC threshold is applied in this example and the FDR threshold is set to 0.10. D) GSE7664 data comparing 8 bt (treated) to 8 med (untreated) samples plotted as in A.\n\nI"}