BMC Genomics 2006, 7:252 http://www.biomedcentral.com/1471-2164/7/252 incorporates one extra round of IVT was used to generate 4 labelled samples using 50 ng of starting material from each pool (Figure 1a). The quality of cRNA from all tissue pools and various treatments was assessed using microflu- idic electrophoresis. Similar profiles were obtained, although there was a significant shift towards smaller RNA fragments with the TwoRA protocol. Material from the 21 target preparations was then hybridized to MOE430A chips. - Data capture and data processing Scanned images were first inspected for quality control (QC) using a variety of built-in QC tools from the Biocon- ductor package [10] of R, the open source environment for statistical analysis. QC consisted of visual examination of probe array images, scatter plots from replicates, hierar- chical clustering of array hybridizations, RNA degradation plots and MvA plots. Feature intensity values from scanned arrays were normalised and reduced to expres- sion summaries using the GC Robust Multiarray Algo- rithm (GCRMA) [11,12] implemented as a function in the Bioconductor GCRMA library [13]. Detection calls indi- cating the presence or absence of signal from each probe set were obtained by processing the raw data with the Microarray Analysis Suite 5.0 (MAS5). To obtain a consen- sus detection call across replicate hybridizations, a probe set was considered to be present if it received a P (present) detection call from all replicates or n-1 replicates with an M (marginal) call from the remaining replicate. A (absent) detection calls were determined in the same way. For further analysis, probesets 3' locations were obtained by downloading the MOE430a probe tab files made avail- able by the Affymetrix online support [39]. A probeset location was considered equal to the 3' distance of the probe that is most distal from the 3' end of the corre- sponding target within the set. To test for differential expression, two statistical methods were used. The bayesian adjusted t-statistics from the lin- ear models for Micoarray data (limma) package [14,15] and the Z-scores method as described by Quackenbush [16]. With both methods, a multiple testing correction based on the false discovery rate (FDR) was performed. Authors' contributions ID participated in experiment design, performed all numerical analysis and produced the manuscript. LW has had an essential input into the statistical analysis of the data and contributed in writing up the manuscript. CAO contributed advice on the analysis and critically reviewed the manuscript. MK designed and implemented the exper- imental work, participated in the analysis and the drafting of the manuscript. All authors read and approved the final manuscript. Acknowledgements We wish to thank Andrew Harrison, Mike Hubank, Caroline Johnston and Klio Maratou for helpful discussions, Danielle Fletcher and Nipurna Jina for preparation of the chips and Nick Jones and Stephen McMahon for help with the spinal cord dissection. This work was supported by the Wellcome Trust's Integrative Animal and Human Physiology Initiative, London Pain Consortium. References 1. Dumur CI, Garrett CT, Archer KJ, Nasim S, Wilkinson DS, Ferreira- Gonzalez A: Evaluation of a linear amplification method for small samples used on high-density oligonucleotide microar- ray analysis. Anal Biochem 2004, 331:314-321. 2. King C, Guo N, Frampton GM, Gerry NP, Lenburg ME, Rosenberg CL: Reliability and reproducibility of gene expression meas- urements using amplified RNA from laser-microdissected primary breast tissue with oligonucleotide arrays. J Mol Diagn 2005, 7:57-64. 3. 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