By analyzing gene-gene correlations in prior experiments, we can identify how many of your differentially expressed genes are normally regulated together and how many may be unique to your particular experimental question. For example, when the top 100 genes up-regulated in the blood of autoimmune patients were analyzed with GO enrichment analysis software, their only strong association was with ribosome-related genes. By looking at how these 100 are normally correlated, two groups emerged – a ribosomal group normally expressed together (red block) and an immune group normally anti-correlated with the ribosomal set (green). This led to a different interpretation of the experiment – that it was not merely a ribosomal “signature” associated with autoimmune flare-ups, but it was increased ribosomal creation plus immune activation (green block) that was associated with the flare-ups (Edgar et al., 2015). This type of analysis can also be used for the analysis of DNA methylation data obtained from the Targeted DNA Methylation & Mitochondrial Heteroplasmy Core
. When analyzing genes whose promoter methylation status changes between experimental and control conditions, an important consideration is to know which of the genes are normally transcriptionally correlated.
Edgar, C.E., Terrell, D.R., Vesely, S.K., Wren, J.D., Dozmorov, I.M., Niewold, T.B., Brown, M., Zhou, F., Frank, M.B., Merrill, J.T. et al. (2015). Ribosomal and immune transcripts associate with relapse in acquired ADAMTS13-deficient thrombotic thrombocytopenic purpura. PloS one 10, e0117614.