MIMIQ: Fast mutual information calculation and significance testing for single-cell RNA sequencing analysis

Abstract

Mutual information is a fundamental quantity in information theory that describes the non-linear dependency between two variables, and has numerous applications within bioinformatics and beyond. However, its exploitation is hampered by a trade-off between computational intensity and accuracy. Here we present an adaptive binning approach to computing the pairwise mutual information, optimized for small integer counts such as those observed in single-cell RNA sequencing. By assuming a sampling distribution such as the negative binomial, a {chi}2 test statistic for hypothesis testing can be computed simultaneously via a copula transformation. Using these quantities, we show how gene rewiring of CD4+ naive T-cells during SARS-CoV-2 infection can be studied using a single-cell sequencing dataset of healthy and COVID-19 donors.