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. 2014 Nov 18;4:7097. doi: 10.1038/srep07097

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(a) Suppose target RNA expression (y_i_,t,d) in sample t of cancer type d is a function of DNA methylation (), copy number () and miRNA regulation (). (b) The expression change across samples for the target RNA is modelled as the response variable in a multivariate linear regression framework using the input variables as indicted above. (c) The resulting linear coefficient indicate the corresponding interaction between miRNA k and target gene i of cancer type d and are transformed into z-scores, which are then subsequently subjected to local false discovery rate (locfdr) estimation¹⁸. (d) The joint posteriors for the recurrent interactions given the z-scores are inferred by empirical Bayes using the probabilistic quantities obtained from the locfdr procedure above.

Inline graphic — (a) Suppose target RNA expression (y_i_,t,d) in sample t of cancer type d is a function of DNA methylation (), copy number () and miRNA regulation (). (b) The expression change across samples for the target RNA is modelled as the response variable in a multivariate linear regression framework using the input variables as indicted above. (c) The resulting linear coefficient indicate the corresponding interaction between miRNA k and target gene i of cancer type d and are transformed into z-scores, which are then subsequently subjected to local false discovery rate (locfdr) estimation¹⁸. (d) The joint posteriors for the recurrent interactions given the z-scores are inferred by empirical Bayes using the probabilistic quantities obtained from the locfdr procedure above.

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