Droplet- based single-cell RNA sequencing (scRNA-seq) datasets typically contain at least 90% zero entries. How can we best model these zeros? Recent work focused on modeling zeros with a mixture of count distributions. The first component is meant to reflect whether such an entry can be explained solely by the limited amount of sampling (on average ~5% or less of the molecules in the cell). The second component is generally used to reflect "surprising" zeros caused by measurement bias, transient transcriptional noise (e.g., "bursty" gene with a short mRNA half life), or true longer-term heterogeneity that can not be captured by a similified (low dimensional) representation of the data. Among others, zero-inflated distributions (i.e., zero-inflated negative binomial) have been widely adopted to model gene expression levels (1, 2).