discovery rate (FDR) analysis (13), we used the ‘qvalue’ R package with default settings (14). To compare proﬁles across platforms, the Pearson correlation was used on non- log transformed data (the RMA data were transformed back from log2), though the Spearman rank correlation yielded very similar results (Supplementary Data). P-values for the test of the hypothesis that a Pearson correlation was equal to zero were tested using the ‘cor.test’ function from R. As an alter- native to presenting scatter plots of comparisons (which are available as Supplementary Data), we have presented results as stratiﬁed histograms. The thresholds for stratiﬁcation were determined by inspection or from the statistical testing, and alternative reasonable thresholds do not change our ﬁndings. All R scripts and data ﬁles used in the analyses are available from the authors. Tissue-specific genes A search was performed of UniGene’s EST database using the digital differential display tool to identify genes with differ- ential expression (Fisher Exact Test, P < 0.05). A comparison was made between placenta genes (library IDs 13037, 13021, 10404, 10403, 10425, 10424 and 10405) and blood, lympho- cytes and lymph nodes (library IDs 13050, 1317, 7038, 7037 and 10312). RESULTS Initial characterization To analyze the ability of each platform to yield reproducible and accurate results, we used a dilution design, outlined in Figure 1 and detailed in Materials and Methods. An initial exploratory overview of the properties of the data is shown in Figure 2, which shows the results of hierarchical clustering of all genes that could be matched across platforms. It is apparent that there are many probes which show strong dilution effects, being overexpressed either in blood or in placenta, on both platforms. Furthermore, there are large numbers of probes which clearly agree across platforms. However, it is also pos- sible to identify clusters of probes which seem to show dilution effects on one platform but not on the other (Figure 2, light bars). The rest of the results we describe ﬁrst considers the dilution effect we observe within each platform and then the comparison across the platforms. To quantify differential expression between placenta and blood, we measured the linear correlation of the designed dilution pattern to the expression pattern of each probe on each microarray (see Materials and Methods). We expected that large numbers of genes would show an effect of mixing of the RNA samples on their relative expression levels, while other genes expressed at equal levels (or not expressed) would not show such a pattern. Figure 3A and B shows the distri- butions of correlations for the two platforms. Both show pro- nounced peaks near correlations of 1 and 1, apparently reﬂecting probes whose targets are differentially expressed between the two samples. A ‘hump’ of points around a cor- relation of zero reﬂects probes which do not show a dilution effect. The Affymetrix and Illumina platforms yielded 35 and 33% of probes with very high dilution effects (absolute value correlations of greater than 0.8), respectively. In terms of tests Nucleic Acids Research, 2005, Vol. 33, No. 18 5917 Figure 2. Hierarchical clustering results of all 36 024 comparable pairs of probes. The dilution step is shown as a graph at the top of the figure (Blood/Placenta). Black bars at the side indicate large clusters of genes that appear to show clear dilution effects in both platforms. Gray bars indicate examples of clusters that appear to show dilution effects in one platform but not consistently in the other. Lighter colors indicate higher relative levels of expression on an arbitrary scale. Note that in this figure, if a gene occurs multiple times on one platform, it is shown in all possible valid comparisons with matching probes on the other platform. of the hypothesis that RNA concentration was not affected by dilution, 56% of Affymetrix and 50% of Illumina probes show signiﬁcant effects [at an FDR < 0.05; the threshold correlations at this FDR are 0.53 (Affymetrix) and 0.55 (Illumina)]. We hypothesized that analyses focused on well- characterized genes would tend to yield better results. Other probes might target predicted genes that turn out to be false positives or pose extra challenges in probe design. Indeed,