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Logo of bmcgenoBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Genomics
 
BMC Genomics. 2009; 10: 439.
Published online Sep 17, 2009. doi:  10.1186/1471-2164-10-439
PMCID: PMC2759969
Relative power and sample size analysis on gene expression profiling data
M van Iterson,1,2 PAC 't Hoen,1 P Pedotti,1 GJEJ Hooiveld,3,4 JT den Dunnen,1,5 GJB van Ommen,1 JM Boer,1,2 and RX Menezescorresponding author1,2,6,7
1Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
2Netherlands Bioinformatics Centre, Nijmegen, the Netherlands
3Nutrigenomics Consortium, TI Food and Nutrition, Wageningen, The Netherlands
4Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University, Wageningen The Netherlands
5Leiden Genome Technology Center, Leiden University Medical Center, Leiden, the Netherlands
6Laboratory of Pediatrics Erasmus Medical Center, Sophia Children's Hospital, Rotterdam, The Netherlands
7Department Epidemiology and Biostatistics, VU Medical Center, Amsterdam, the Netherlands
corresponding authorCorresponding author.
M van Iterson: M.van_iterson.HG/at/lumc.nl; PAC 't Hoen: P.A.C._t_Hoen/at/lumc.nl; P Pedotti: paola.pedotti/at/gmail.com; GJEJ Hooiveld: Guido.Hooiveld/at/wur.nl; JT den Dunnen: ddunnen/at/HumGen.nl; GJB van Ommen: G.J.B.van_Ommen/at/lumc.nl; JM Boer: J.M.Boer/at/lumc.nl; RX Menezes: r.menezes/at/vumc.nl
Received February 19, 2009; Accepted September 17, 2009.
Abstract
Background
With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental.
Results
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 PPARα agonists were administered to wild-type and PPARα-null mice. Our analysis confirms the hierarchy of PPARα-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 δC-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.
Conclusion
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.html.
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