Methods that differentially eliminate data that are likely to be unreliable can be of great help to the investigator. Not all genes are expected to be expressed at levels that are either biologically significant or detectable by the Affymetrix technology (1–3 copies per cell) in any particular tissue; in fact, the subset of genes expressed is what determines the characteristics of each tissue. For example, Jongeneel, et al. [3] estimated that 10,000–15,000 transcripts are expressed in human cell lines at one copy per cell or above. Data for genes not actually expressed represent experimental noise and cannot increase true positives, but can (and do) generate false positives. Discarding data for genes that are not expressed at detectable levels is, therefore, justified by biology and should result in an improvement in the balance between true and false positives.
