While intact RNA obviously constitutes the best representation of the natural state of the transcriptome, there are situations in which gene expression analysis may be desirable even on partially degraded RNA. Some studies report collection of reasonable microarray data from RNA samples of impaired quality (28) , leading to meaningful results if used carefully. Moreover, Auer et al. (24) recently concluded that degradation does not preclude microarray analysis if comparison is done using samples of comparable RNA integrity. We confirmed the direct influence of the RNA quality on the distribution of gene expression levels, by detecting using Q-PCR a significant (up to 7-fold) difference in the relative expression of genes in samples of slightly decreased RNA integrity, which is much larger than the variation within comparable RNA quality categories (cf. Figure 5 and Table 2 ). This may correlate with ratio discrepancies in gene expression experiments, and therefore with false positive and false negative rates of differential gene expression when comparing two samples. Therefore, computing reliable metrics of RNA integrity, even if the RNA is found to be partially degraded, may be highly valuable. The straight and unambiguous relationships established between human interpretations and both RIN and DegFact distributions indicates that, using these metrics, it should be possible to distinguish specific samples that are too disparate to be included in comparative gene expression analyses without compromising the results. Although the information provided by these user-independent classifiers is not a guarantee for successful downstream experiments, it gives a more comprehensive picture of the samples and can be used as a safeguard against performing useless and costly experiments.