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We assessed the statistical significance of the differential expression of genes by computing a q-value (i.e. minimum FDR) for each gene (Table 1).
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To compute a q-value, we first computed a False Discovery Rate (FDR), which measures the expected fraction of true positives for any given p-value threshold p0 [53].
In particular, we compute a q-value for every pair X Y that we test, and deem every test with a q-value less than some threshold q0 to be a real association.
Finally, we compute a q-value using the Benjamini Hochberg method.
To correct for the multiple hypothesis testings, we used the Benjamini Hochberg procedure implemented in the R function p.adjust, which computes a q-value for each intersection.
For each PSM, we then compute a q value, which is defined as the minimal FDR threshold at which the PSM is deemed significant (Storey and Tibshirani, 2003).
Genotype-methylation associations were adjusted for multiple comparisons by computing the FDR q-value.
For our unsupervised signature, the expression difference between groups of Gbs was assessed by computing the q-value for all probesets in our local data set (Storey and Tibshirani, 2003).
Significance was defined by a Q-value <0.01 and using default values for the remaining parameters.
We corrected for multiple testing by computing the q value for each locus, using the QVALUE package (Storey 2002) in R (R Core Team 2012), using a q value of 0.05 as a threshold for significance.
We corrected for multiple testing by computing the q value for each locus, using the QVALUE package (Storey 2002) in R (R Core Team 2012) and calculated empirical P values, as described above.
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