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Using multiple approaches, we built a collection of 323 gene expression modules, including 115 gene lists obtained from 53 publications: (1) 221 modules were built using the median expression of all genes within the module that homogeneously expresses these genes (i.e. all genes in the module were high or low together within a given sample).
Using the median expression values for miR-34c and miR-422b as respective cutoffs, high grade serous carcinomas were separated into high-expression group (expression values > median value) and low-expression group (expression values ≤ median value) for Kaplan-Meier survival analysis (Figure 4).
The expression values for duplicated probes with the same Unigene cluster ID were collapsed using the median expression value.
Patients were divided into high or low expression groups using the median expression values for each cyclooxygenase gene.
For survival analysis, the CHAC1 mRNA expression was dichotomised into low and high using the median expression value.
The expression of pS6 and VEGFR2 Tyr996) was dichotomised using the median expression as a cutoff value.
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For expression level we used the median expression across tissues; for the expression breadth or pattern, the variability between tissues; and for noise, the variability between biological replicates.
To account for the expression values for all probe sets while minimizing the effect of erroneous values, as the default, we used the median expression value across the probe sets for a given gene.
We use the median expression value for all genes inside the module as the representative expression value for the module in a given sample.
As a summary statistic for multiple probesets that match to the same entrez gene identifiers we used the median expression value.
We first used the median expression data of the genes in the eight samples to compute pairwise correlation coefficients in the R statistical environment, resulting in a correlation matrix of 12353 × 12353.
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