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).

Note: The content above has been extracted from a research article, so it may not display correctly.



Q&A
Please log in to submit your questions online.
Your question will be posted on the Bio-101 website. We will send your questions to the authors of this protocol and Bio-protocol community members who are experienced with this method. you will be informed using the email address associated with your Bio-protocol account.



We use cookies on this site to enhance your user experience. By using our website, you are agreeing to allow the storage of cookies on your computer.