Over detection was measured as the area between the ROC-like curve for differentially expressed genes detected using cross-platform normalized data and the intersection of that curve with the curve representing the union of genes detected using each platform independently. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those FG-2216 statistics. The consistency MGC18216 of platform effects across studies is explored theoretically and with respect to the testing data sets. == Conclusions == Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods FG-2216 do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment organizations are equally sized, while DWD is definitely most powerful to differently sized treatment organizations and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of carrying out the nine cross-platform normalization methods considered. The package can be downloaded athttp://alborz.sdsu.edu/conorand is available from CRAN. == Background == Simultaneous measurement of gene manifestation on a genomic scale can be accomplished using microarray technology or by sequencing centered methods [1-3]. Many high-throughput mRNA manifestation experiments produce data that can be of value to other experts when analyzed in fresh contexts or in combination with data from additional experiments. In particular, the statistical power and reproducibility of gene manifestation studies can be improved by combining data across multiple studies [4-6]. While next generation sequencing seems likely to replace microarrays for manifestation analysis in the near future, the large amount of microarray data already in existence could continue to be useful to experts for many years to come. Modern microarrays are commercially produced, and one-color hybridization techniques are often used. Several companies possess emerged as leading manufacturers, each using different developing techniques, labeling methods, hybridization protocols, probe lengths, and probe sequences. Table1lists some important characteristics of the FG-2216 microarray platforms analyzed with this work. These characteristics can affect microarray overall performance [7-10]. The space of probes represents a tradeoff between level of sensitivity and specificity, with longer probes becoming generally more sensitive and shorter becoming more specific. The use of linkers to reduce steric hindrance, as FG-2216 employed by the Applied Biosystems and Illumina platforms in table1is one method for increasing the level of sensitivity of short probes. The method by which probes are constructed and attached, and the overall construction of the array, can affect probe uniformity and intra-platform reproducibility. Labeling and detection chemistry impact the dynamic range of detection. == Table 1. == Characteristics of relevant microarray platforms Characteristics of microarray platforms FG-2216 analyzed with this work. See referrals [7-10] for info sources. Chemiluminescence provides higher level of sensitivity for low levels of manifestation compared to fluorescence, but at the risk of saturation for highly indicated genes. The Applied Biosystems scanning procedure efforts to mitigate scanner saturation by using both a short and a long exposure to lengthen the dynamic range of its manifestation measurements. Probe sequences impact the binding constants between probes and target and non-target molecules, and therefore the level of sensitivity and specificity of each probe depends partially on its sequence. Salinity and composition of the hybridization remedy, temperature, and incubation time of hybridization may also impact level of sensitivity and specificity. Data from two microarrays are directly comparable only if those microarrays are identical in all design guidelines including probe sequences and have been subjected to similar hybridization conditions. Because no two platforms share the same set of probe sequences, no two platforms produce data that are directly similar, actually if all other variables are the same. For experimenters this restriction is not major. They need only ensure that all experiments are carried out using the same array platform and protocol. However, platform effects pose a significant problem for the re-analysis of data from multiple microarray studies. Experts who perform high throughput gene manifestation assays often deposit their data in public databases such as ArrayExpress [11] and Gene.