DREMI and DREVI are information theory–based methods developed to quantify and visualize relationship between two molecular epitopes (19). Given two proteins epitopes X and Y, and assuming that we are interested in assessing the influence of X on Y, then DREVI visualizes the conditional dependence of Y on X. Specifically, DREVI computes the conditional probability density of Y given X, p(YX)=p(X,Y)p(X), where the joint distribution is computed using a heat diffusion–based kernel density estimation procedure (68). Once the conditional density is computed, the result is visualized as a heatmap.

DREMI quantifies the strength of relationship between two protein epitopes, using mutual information–based metric. Although a traditional mutual information metric relies on the joint distribution, it is more likely to be biased by dense regions, thereby missing out on interesting biology shown by extreme amounts of proteins. To circumvent this, DREMI computes the mutual information on the conditional density function (as computed for the DREVI visualization) as opposed to the joint density.

Thus, DREMI=Ic(X,Y)=ijp(yjxi)log(p(xi,yj)p(xi)p(yj))

Effectively, DREMI reweighs the regular mutual information so that all observed range of expression contributes uniformally. In this article, we used the simpledremi implementation of DREVI and DREMI for analysis (69).

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