After quantile normalization (using limma package in R) on the age arrays (25 young and 26 older subjects, Table S1), we first determined the number of genes significantly differentially expressed with age, by performing on a probe-by-probe basis, 24354 two-sample t-tests.
Analysis of variance (ANOVA) was performed using a National Institute on Aging Array Analysis Tool (18).
The "total" data were imported into the online National Institute on Aging Array Analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/index.html), where they were stratified by disease state (normal, intact OA, and fibrillated OA) and subjected to one-way analysis of variance.
A similar linear mixed-effects model was fitted on the blood DNA methylation data in these individuals with age, BMI, array, position on the array, and granulocytes, monocytes, CD8 T cells (estimated) as fixed effects, and family and zygosity as random effects.
We then regressed out the Houseman-estimated white blood cell proportions, age, sex, array number and position on array on the Dasen normalized beta-values of the CpG sites using a linear mixed model.
These results are directly comparable as samples from mice of the same age were arrayed on the same chip.
Data was processed and analysed using the National Institute on Aging (NIA) array, The Database for Annotation, Visualisation and Integrated Discovery (DAVID), GenePattern, and visual anaysis tool (visANT) software.
Paired comparisons using National Institute on Aging (NIA) array analysis determined a correlation greater than 0.98 for eEPC biological replicates, and greater than 0.97 for OEC replicates (Additional File 1 Table S2).
Chicken microarrays were employed, each using pooled RNA samples (n = 6 birds, within each of two breeds and at 4 or 5 ages; 9 arrays in all).
Similarly, array age did not account for a significant amount of variation in array CVs.
