%0 Journal Article
%T Effects of filtering by Present call on analysis of microarray experiments
%A Jeanette N McClintick
%A Howard J Edenberg
%J BMC Bioinformatics
%D 2006
%I BioMed Central
%R 10.1186/1471-2105-7-49
%X Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p ¡Ü 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3¨C5 samples per treatment group) benefit from more restrictive filtering (¡Ý50% Present).Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives.Affymetrix GeneChips£¿ are routinely used to measure relative amounts of mRNA transcripts on a genome wide basis. The large number of probe sets (representing genes) available on these arrays gives the researcher a wealth of information, but the multiple testing raises the potential for a large number of false positives. False positives and false negatives can both pose problems for the researcher, each with its own cost, so the balance between the two should be evaluated based upon the goals of the experiment. Increasing the stringency for accepting differences as significant (decreasing p-value) reduces false positives, which is important if verification and follow-up a
%U http://www.biomedcentral.com/1471-2105/7/49