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, coefficient of variance (CV), CV2, and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is independent of dataset size. In contrast, the Gini index displayed paradoxical behavior in that it increases as dispersion decreases, and Shannon entropy was not scale-invariant. Next, we applied the variance-to-mean ratio (Fano factor) to measure transcriptional variability in single-cell datasets representing different complex systems and cross-platform measurements. Our data show that many genes display transcriptional variability within the same cell type, and that while variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age, variable genes are often correlated with specific biological processes. Notably, most genes and pathways with highest transcriptional variability as identified by the Fano factor were largely independent of differentially expressed genes and have also been implicated in biological processes related to the system. Thus, our data highlight that choice and application of appropriate models for measuring transcriptional variation in scRNA-seq data can reveal biologically relevant information beyond what is observed from mean expression alone.Author summary: Single-cell RNA sequencing (scRNA-seq) data allows for the study of transcriptional variability. However, the contribution of transcriptional variability to gene expression has not been fully appreciated in part due to a lack of consensus on how to estimate and apply noise metrics for downstream analytical modeling. The study of transcriptional variability provides a new lens through which we can study how transcriptional dynamics impact complex biological phenomena. Here, we simulated single-cell data to test six dispersion metrics for their relative sensitivity to variability in single-cell counts. From our simulations, we found that the variance-to-mean ratio (VMR or Fano factor) appears to be the most suitable metric among those tested for quantifying transcriptional variability as it is scale-invariant and is easily interpretable with respect to changes in data dispersion. We then applied the VMR to analyze changes in transcriptional variability in scRNA-seq datasets from platforms with different capture rates. We find that the Fano factor can identify genes distinct from differentially expressed genes and that variable genes relate to specific functional categories that likely reflect the underlying biology. For most distributions, VMR/Fano factor is a reasonable, robust choice for modeling transcriptional noise. However, for certain niche distributions, other metrics may be better suited. Together, we demonstrate that model choice for measuring transcriptional variability can provide new biological insights into how cells respond and adapt in complex systems.