%0 Journal Article
%T Relative power and sample size analysis on gene expression profiling data
%A M van Iterson
%A PAC 't Hoen
%A P Pedotti
%A GJEJ Hooiveld
%A JT den Dunnen
%A GJB van Ommen
%A JM Boer
%A RX Menezes
%J BMC Genomics
%D 2009
%I BioMed Central
%R 10.1186/1471-2164-10-439
%X Relative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPAR¦Á agonists were administered to wild-type and PPAR¦Á-null mice. Our analysis confirms the hierarchy of PPAR¦Á-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal ¦ÄC-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in detecting differences in the low intensity range, compared to microarray platforms.Power and sample size analysis based on pilot data give valuable information on the performance of the experiment and can thereby guide further decisions on experimental design. Solexa/Illumina deep sequencing is the technology of choice if interest lies in genes expressed in the low-intensity range. Researchers can get guidance on experimental design using our approach on their own pilot data implemented as a BioConductor package, SSPA http://bioconductor.org/packages/release/bioc/html/SSPA.htm
%U http://www.biomedcentral.com/1471-2164/10/439