5920 Nucleic Acids Research, 2005, Vol. 33, No. 18 identify genes that might show a differential expression effect: an independent ‘gold standard’ would be desirable. While we are not aware of any large validated set of placenta- or blood-speciﬁc genes, from public databases we were able to obtain a set of 174 genes that are predicted to be placenta or blood speciﬁc (see Materials and Methods), and should there- fore show a strong dilution effect. As shown in Figure 5C, these genes show excellent agreement across the platforms, with many fewer disagreements than the data considered at large (Figure 5A). Very similar results overall were obtained when using annotations provided by the manufacturers (Supplementary Data). As mentioned, the level of expression would be an impor- tant factor in making a good comparison: if a gene is simply not present in the samples, the measurements will be just noise, and we do not expect noise to be similar across plat- forms (by deﬁnition). More generally, we expect higher exp- ression levels to be associated with less noisy measurements, and therefore would yield better agreement across platforms. That this is indeed the case is shown in Figure 6A; the rank correlation of expression level to measure cross-platform agreement is 0.37–0.43 (depending on whether the Illumina or Affymetrix expression levels, or their means, are used for evaluation). Genes that are expressed at low levels are not as likely to be reproducible across platforms. This effect is main- tained even after ﬁltering out genes that show no or weak dilution effects as described above (comparisons were retained if at least one probe showed a signiﬁcant dilution effect at an FDR of 0.05; Supplementary Data). This is because many of the weakly expressed probes do show (apparent) dilution effects. We further hypothesized that for two probes to agree across platforms, they should be measuring the same biological entity (transcript or set of transcripts). While our annotation system tries quite hard to identify which transcript or set of transcripts a probe is likely to hybridize to, thereby identifying cases where we believe the platforms are measuring the same RNA, the resolution of this approach is limited. Speciﬁcally, probes that are expected to hybridize to different parts of the same transcript might yield different signals. This could be due to differential detection of degraded messages, or limita- tions in annotations. As an example of the latter situation, if one probe targets a previously unannotated transcript while another does not, our system might measure them as assaying the same transcripts while there is in fact a difference. To test this idea in a simple way, we plotted the relationship between cross-platform agreement and cross-platform probe spacing. As shown in Figure 6B, when Affymetrix and Illu- mina probes align to very close or overlapping locations in the genome, they have a tendency to agree more, whereas probes that hybridize to distinct locations, even along the same gene, tend to disagree more. Compared with the effect of expression level, the effect is small though still highly statistically sig- niﬁcant, with a rank correlation of 0.18. However, this con- clusion is complicated by the fact that expression level is also affected by distance from the 30 end (rank correlation 0.15), so the measure of probe location difference is not independent of the level of expression. If we analyze only probes that have higher expression levels (e.g. Affymetrix expression level log2 > 7), the effect of location on agreement is enhanced slightly (rank correlation 0.25), indicating that the effect can- not be completely attributed to associated differences in expression level. As for the effect of expression level, the effect remains after removing probes which failed the dilution effect ﬁlter. Within-platform reproducibility Some additional insight into the reproducibility problem comes from looking at reproducibility within each platform. On the Affymetrix platform especially, there are often multi- ple probes per gene (Table 1). It is expected that in many cases this redundancy is intended to address transcript divers- ity within a gene, but these data points provide a situation Figure 6. Cross-platform agreement measured by the rank correlation of expression levels as a function of expression level (log2) (A) and distance between probes in base pairs (B). For complete data see the Supplementary Data.