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One of the signatures in the microarray assessment was a relatively large number of up regulated putative cuticle proteins.
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Microarray quality-control assessment was carried out using the AffyPLM R-package [ 32].
Microarray quality-control assessment was carried out using the R AffyPLM package available from the Bioconductor web site (, [ 50]).
Microarray quality control assessment was performed using the R AffyPLM and SimpleAffy packages available from the Bioconductor web site.
The current state and future needs for application of toxicogenomic (TGx) technologies, including microarray analysis, to risk assessment was summarized recently in a National Academy of Sciences NASS) publication [ National Research Council NRCC) 2007].
To determine the efficacy in correcting for technical biases and improving comparability between microarrays, quality control and bias assessment was performed on all datasets before and after normalization for each of the six normalization approaches.
Data assessment was done using Affymetrix Microarray Suite 5.0.
That assessment was premature.
Many of the currently used microarray quality assessments are manufacturer-based recommendations that rely on a limited number of parameters with imprecise specifications for acceptable results.
Microarray-based gene expression assessment is a very useful method for prediction of diseases, tumor classification and drug responses.
As microarray data quality can be affected at each step of the microarray experiment processing (Schuchhardt et al., 2000), quality assessment is an integral part of the analysis.
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