Measuring single-cell level gene expression stability and variability in healthy and severe COVID-19 patients using Kullback–Leibler divergence

Abstract

Recent advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the acquisition of RNA at the single-cell level, which showed that the expression level of genes is highly variable across and within the cell types. Even well-known housekeeping genes showed high expression variance in a single condition and within the same cell types. Previous studies made efforts to identify stably expressed genes and use them as a yardstick for robust gene expression normalization. On the other hand, drugs were shown to be less effective on genes with high expression variance. Thus, identifying both stably and variably expressed genes is an important task, especially at the single-cell level. In this study, using the Kullback-Leibler divergence method, we proposed a metric to measure the expression stability of each gene. Using private scRNA-seq data composed of 25 severe COVID-19 patients and 40 healthy individuals, we identified variably expressed genes specific to COVID-19-infected patients and healthy cohorts.