Correlation to the N-addition microarray data set was determined by Pearson Product-Moment Correlation while correlations to the P-addition data set were determined by Spearman's Rho, due to a non-normal data distribution, using JMP version 5.1.2 (SAS Institute, Cary, NC).
To validate the microarray data, we determined gene expression levels by quantitative real-time PCR (qPCR) using dual-labelled probes.
Interestingly no relationship was observed between the reproducibility of the expression pattern and the significance of the microarray data as determined by ANOVA.
The quality of the microarray data was determined by MA plots whereas the reproducibility and homogeneity between the experiments was assessed respectively by Box plots and Density plots.
To increase confidence in the biological interpretation of our microarray data, we determined the concentrations of three cytokines and a chemokine (IL-10, IL-6, IL-1β and MIP-1β), using a multiplex kit.
The final Δ so2426/WT ratio for each target gene and the standard error were calculated as described elsewhere [ 31], and the linear correlation between the qRT-PCR and microarray data was determined based on the log mean values using Sigma-Plot version 9.0 (SPSS Inc., Chicago, IL).
Two different methods were applied to the microarray data to determine if a given gene was present or absent.
These results varied from the FACS data by a mean of 1.3%, illustrating that deconvolution of expression microarray data accurately determines the levels of memory and naïve subsets of T lymphocytes in blood samples.
Therefore, we examined our microarray data to determine whether the stress-resistance genes regulated by the daf-2 and age-1 pathway were autonomously or non-autonomously regulated (Table S3).
Recently, Chuchana et al. integrated gene pathway information into microarray data to determine the threshold for identification of DEGs [ 32].
