Information-theoretic analysis of multivariate single - cell signaling responses using SLEMI.
Mathematical methods of information theory constitute essential tools to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm to analyze signaling data within the framework of information theory. Our approach enables robust as well as statistically and computationally efficient analysis of signaling systems with high-dimensional outputs and a large number of input values. Analysis of the NF-kB single - cell signaling responses to TNF-a uniquely reveals that the NF-kB signaling dynamics improves discrimination of high concentrations of TNF-a with a modest impact on discrimination of low concentrations. Our readily applicable R-package, SLEMI - statistical learning based estimation of mutual information, allows the approach to be used by computational biologists with only elementary knowledge of information theory.
Publisher URL: http://arxiv.org/abs/1808.05581
Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.
Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.