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Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association

Figure 3

Predicting TF-TF interactions using shared modules as a measure of shared function.

(A): Prediction of (i) gene expression correlation, (ii) literature mentions, and (iii) shared functional annotations using a Naive approach, shared TFICA modules, and weighted TFICA modules. The Naive approach (“Naive”) links TFs to TFs by the similarity of their ChIP-Seq targets, “TFICA” links TFs to TFs by the similarity of their significantly associated modules, and weighted TFICA weights these modules in the similarity by their confidence. β coefficients in a linear model are shown with 95% confidence intervals. In each case, TFICA and weighted TFICA significantly outperforms the Naive approach. In addition, we used permutation testing to validate these results. In each case (expression, literature, function) the β coefficient for the permuted model was not significant (βexp = 0.16; 95%CI −0.02–0.34; βlit = −0.02 95%CI −0.08–0.05; βfun = −0.04 95%CI −0.14–0.06, P>0.05 for each). Data not drawn. (B): The top 30 highest-scoring pairs are shown, as measured by target module similarity, 14 of which are known associations (solid lines). Many of these factors form a tight sub-network of activators and repressors.

Figure 3