Assessment of dispersion metrics for estimating single-cell transcriptional variability

Single-cell RNA sequencing data enables analysis of transcript levels of single cells across different cell types and conditions. Recent work has highlighted the value of measuring gene-specific transcriptional variability, or noise, within a genetically identical population of cells in addition to mean expression given that these differences contribute to biological processes including development and disease. However, measuring transcriptional noise remains a challenge. Here, we systematically compared statistical methods by simulating single-cell data by varying both dispersion and count size to assess the relative responsiveness to noise of several commonly used statistical metrics: the Gini index, variance-to-mean ratio, variance, and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is scale-invariant. In contrast, the Gini index displayed paradoxical behavior, and Shannon entropy was not scale-invariant. Thus, we next applied the variance-to-mean ratio to measure transcriptional variability in a publicly available single-cell dataset of embryonic hearts from a mouse model of maternal hyperglycemia. Our data show that many genes display transcriptional variability within the same cell type, and that this variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age. Notably, many of the genes and pathways with highest transcriptional variability were not identified as differentially expressed, and have in fact been implicated in maternal hyperglycemia in other studies, suggesting that transcriptional variability can provide additional biologically relevant information beyond what is observed from studying mean expression alone.