For certain genes in a microarray experiment, even if the null hypotheses can be accepted when using T-test, F-test, or SAM, skewness and the kurtosis for those genes can be significantly different, indicating the distributions of both case and control are asymmetric and leptokurtic/platkurtic.
Additional data sets helping to elucidate gene function are expression data, either from DNA microarray experiments, SAGE experiments or even from large-scale reporter gene expression studies [ 19, 20].
In Additional files 3 and 6 we provide the genes most strongly affected in our dataset; these genes can be used as informative stress markers for pre-evaluating future sample collections, even before microarray experiments.
Unlike earlier Gene Set Enrichment Analysis methods [ 59], MR-GSE can be used to test individual gene sets in isolation and has good power even for microarray experiments with small to moderate sample sizes.
These analyses underscore the need for replica experiments to reliably interpret large-scale expression data sets, even with simple microarray experiments.
It has been shown that most of the variability in microarray experiments derives from the biological sample itself, even when conducted under apparently identical conditions.
Clearly, validation of results will be even more critical in cross-species microarray experiments because of questions surrounding cross-hybridisation of related genes under conditions of reduced stringency.
The HESI experiments, published in the March 2004 toxicogenomics issue of EHP, showed the patterns of gene expression detected in microarray experiments can give insight into biologic mechanisms, and can even distinguish between damage found in different cell types.
