Supporting data for "scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data."

Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability, therefore, are useful for going beyond changes based on average expression and instead, identifying genes with homogenous expression versus those that vary widely from cell to cell. <br>We present a novel statistical framework <i>scShapes</i> for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by over-dispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. <i>scShapes</i> quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that <i>scShapes</i> identifies subtle variations that are independent of altered mean expression and detects biologically-relevant genes that were not discovered through standard approaches. <br>This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from <i>scShapes</i> help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework <i>scShapes</i> is incorporated into Bioconductor R package (https://github.com/Malindrie/scShapes)