W640 Nucleic Acids Research, 2005, Vol. 33, Web Server issue Table 1. Graphical outputs and their functions for the RACE Quality Checks & Normalization tool Output CEL intensity images Boxplots of raw PM probe intensities Density distribution of the raw PM probe intensities Boxplots of the normalized PM intensities 50 to 30 feature intensity plot (‘RNA digestion plot’) PLM pseudo images NUSE boxplots RLE boxplots Pair-wise scatter plots Correlation matrix plot Sample cluster Function Detect spatial intensity artifacts Display PM probe intensity distribution for selected array; compare overall brightness of selected array Check the intensity density for selected arrays; compare the densities of selected arrays Assess the success of the normalization Detect a bias in probe intensities; identify outlier arrays with deviating biases Assess spatial distribution of weights derived during robust linear model probe-level fit; detect obscure/ dark array regions with low weights; assess the spatial distribution of residuals; detect obscure/dark array regions with high positive or negative residuals Identify arrays where the standard errors for gene expression estimates from PLM fit are overall larger relative to other arrays Identify arrays where relative log expression compared with a median array are larger than other arrays Assess similarities and differences in expression values measured on two arrays; identify outliers arrays Detect homogeneous groups of arrays; identify outlier arrays Find subgroups of similar samples Figure 2. ‘Bias 50 to 30 end plot’ tool output. Each line represents the overall 50 to 30 intensity bias of a different chip. An example of the output type ‘Bias 50 to 30end plot’ is shown in Figure 2. Here, each line corresponds to an individual array. The graph is generated by calculating and plotting the array-wide mean intensities of ordered PM probe sets, where position 0 corresponds to the most 50 probe and position 10 the most 30 probe (the data are from an Affymetrix HGU133A array, whose probe sets each contain 11 PM probes). The slope and shape of each line is characteristic of each target sample and is dependent on the RNA sample source and the array type. When comparing expression data from a group of hybridiza- tions, a sample whose slope and shape deviate signiﬁcantly from the rest will often have anomalous ‘outlier’ results. Owing to space limitations, the content and purpose of all other output graphs can be only brieﬂy summarized in Table 1. Statistical Tests tool Purpose and required data input format. The Statistical Tests tool identiﬁes genes which are differently expressed between two groups. The input ﬁles for this tool are two expression matrices provided as tab-delimited ASCII ﬁles. The ﬁrst col- umn of both ﬁles must contain unique gene identiﬁers and all other columns contain normalized expression values of the samples corresponding to the different groups. The input ﬁles can be generated on the server by splitting the output ﬁle ‘NormExprLevels.txt’ from the ﬁrst tool into two groups. Description. The design of gene expression experiments can be represented in terms of a linear model (18). At the moment RACE supports designs where two groups are compared to identify genes changing expression across the groups. RACE uses the Bioconductor package ‘limma’ (http://bioinf.wehi. edu.au/limma/usersguide.pdf), which makes use of an empir- ical Bayesian approach, to ﬁt the linear model. This approach outperforms a conventional t-test under conditions typical for microarray experiments (18–20). Owing to the large number of genes analyzed in a typical microarray experiment, an assessment of the effect of multiple testing is necessary. Therefore, we estimate from the distribution of raw p-values the fraction of the non-changing genes among all tested genes, as well as the false discovery rate (FDR) for each p-value threshold using the Bioconductor package ‘qvalue’ (21,22). Output. The principle output are lists of potentially differen- tially expressed genes chosen according to user-speciﬁed fold-change and p-value thresholds. A separate overview list containing all genes, complemented by statistical measures and additional gene annotations (e.g. GeneSymbol and LocusID), is also provided. RACE determines for each gene the fold-change, the logarithm of the fold-change (M), the mean expression level (A), the uncorrected p-value, the estim- ated FDR, the regularized t-value, the log odds ratio (B) and the standard deviations of the expression levels in each group. RACE provides multiple ways of visualizing these values. See Table 2 for an overview of the output graphs. Figure 3 shows an example of the output type ‘p-Value histogram’ with an inset displaying the dependency between the FDR and the p-value. The p-value distribution is expected to be uniform if there are no differentially expressed genes. As the number of differentially expressed genes increases, the p-value distribution will show a more and more pronounced peak at small values. Figure 3 shows the output from a com- parison of human testis and placenta RNA. A sharp, very high peak at small p-values is seen, indicating many highly