proposeInteractions

affinities

cutoff.lower

cutoff.upper


The purpose is to identify interesting interactions terms for our model. Assuming the design matrix to be binary, the interaction term between two variables X1 and X2 is 1, if X1=1 AND X2=1 (zero, otherwise). The function computes the relative overlap between all pairs of variables X1 and X2 via the Tanimoto-Jaccard index. Variable pairs with cutoff.lower < Tanimoto-Jaccard < cutoff.upper are considered interesting candidates.


Expression levels of mRNA molecules are regulated by
different processes, comprising inhibition or activation by
transcription factors and post-transcriptional degradation by
microRNAs. biRte uses regulatory networks of TFs, miRNAs and
possibly other factors, together with mRNA, miRNA and other
available expression data to predict the relative influence of
a regulator on the expression of its target genes. Inference is
done in a Bayesian modeling framework using
Markov-Chain-Monte-Carlo. A special feature is the possibility
for follow-up network reverse engineering between active
regulators.

Holger Froehlich

birte

Bayesian Inference of Regulatory Influence on Expression (biRte)

proposeInteractions function

The purpose is to identify interesting interactions terms for our model. Assuming the design matrix to be binary, the interaction term between two variables X1 and X2 is 1, if X1=1 AND X2=1 (zero, otherwise). The function computes the relative overlap between all pairs of variables X1 and X2 via the Tanimoto-Jaccard index. Variable pairs with cutoff.lower < Tanimoto-Jaccard < cutoff.upper are considered interesting candidates.

proposeInteractions: Propose possible regulator-regulator interactions that could be worthwhile to be tested into the biRte model.

Description

Usage

Arguments

Value

Examples