We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets.
Following the phenotype microarray analysis that revealed resistance towards various heavy metals, a series of determinants were also identified in the genome of S13.1.2 (Fig. 2; Additional file 1: Table S1).
lc1-D is insensitive to IAA and hypersensitive to exogenous BR, in agreement with the microarray analysis that reveals the altered transcriptions of genes involved in auxin signaling and BR biosynthesis.
We also identified genes in our microarray analysis that were down-regulated by androgens.
To compare fibroblasts from subjects with HGPS to unaffected controls, we selected genes identified in the microarray analysis that were upstream regulators in the signaling pathway.
Validation was done in a randomly selected subset of the original samples (submitted for microarray analysis) that included nine healthy controls and twelve PC patients.
A subset of differentially expressed genes identified by microarray analysis that have well-characterized roles in spermatogenesis and chromatin regulation is listed in Table S1.
Briefly, this is a systems-scale strategy for microarray analysis that has identified transcriptional modules formed by genes coordinately expressed across multiple disease data sets thus allowing functional interpretation of the microarray data into biologically useful information.
These ISGs were chosen based on an earlier microarray analysis that revealed constitutive (i.e., IFN-independent) expression of their mRNAs to be down-regulated in EBV-positive Akata cells relative to their EBV-negative counterparts (I.K.R. and J.T.S., unpublished observation).
These results are consistent with those from a recent microarray analysis that found none of the canonical courtship genes changed expression in male flies after brief exposure to females.
The restriction on the number of genes we analyzed, given a modest number of samples, circumvents methodological problems associated with other large scale expression studies such as microarray analysis that often preclude proper statistical analysis due to a high false discovery rate and huge family-wise errors.
