Information-theoretic analysis of multivariate single - cell signaling responses using SLEMI

TL;DR

This paper introduces SLEMI, a new R-package and algorithm that efficiently analyzes high-dimensional single-cell signaling data using information theory, revealing how NF-kB responses discriminate TNF-a concentrations.

Contribution

The paper presents a novel, robust, and computationally efficient method for analyzing multivariate signaling data with high-dimensional outputs using information theory.

Findings

NF-kB signaling improves discrimination of high TNF-a concentrations.

SLEMI enables analysis of complex signaling systems with many inputs and outputs.

The approach is accessible to biologists with basic information theory knowledge.

Abstract

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.