For performing gene expression profile analysis do the following steps:
Averaging expression intensities of all replicates for control and experimental group within each dataset.
Optionally: Probes with low intensity values can be removed from the analysis (e.g. whose intensities are lower than the 50th percentile on the respective array) in order to increase reliability of the results and achieve a better concordance between expected ratios/fold-changes and observed ratios/fold-changes (Choe et al. 2005).
Calculation of expression ratio and/or fold-changes.
Comparison of the expression values via a two-tailed Student's t test to incorporate data on experimental variability and assesses whether there is a significant difference.
Usually genes are rendered as differential expressed if their expression value is altered by more than 1.5-fold and the p-value below 0.05.
Additionally the effect size for each transcript can be computed. The effect size is a simple way of quantifying the differences between two groups that has many advantages over the use of statistical significance alone. Particularly effect size emphasis the size of the difference rather than confounding this with the sample size. The effect size is simply the standardized mean difference between two groups:
Effect size = ( [mean of experimental group] - [mean of control group] ) / Standard Deviation
S.E. Choe, M. Boutros, A.M. Michelson, G.M. Church, M.S. Halfon, Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset, Genome Biol, 6 (2005) R16.
Denigma expressions is there to integrate transcriptomics, proteomics and metabolomics expression changes with the use of the powerful concepts of molecular Profiles and Signatures, Set Theorie (i.e. Intersections) as well as sophisticated Meta-Analysis.