ARACNe, an information-theoretic algorithm for inferring transcriptional interactions, was used to identify candidate transcriptional regulators of the transcripts annotated to genes in both CMC and CNON data. First, mutual interaction between a candidate TF(x) and its potential target (y) was computed by pairwise MI, MI(x, y), using a Gaussian kernel estimator. A threshold was applied on MI based on the null hypothesis of statistical independence (P < 0.05, Bonferroni-corrected for the number of tested pairs). Second, the constructed network was trimmed by removing indirect interactions using the data processing inequality (DPI), a property of the MI. Therefore, for each (x, y) pair, a path through another TF(z) was considered, and every path pertaining to the following constraint was removed: MI(x, y) < min(MI(x, z),MI(z, y)). A P value threshold of 1 × 10−8 using DPI = 0.1 [as recommended (17)] was used when running ARACNe.

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