  Single-cell RNA-seq data are challenging because of the sparseness of the
read counts, the tiny expression of many relevant genes, and the variability in
the efficiency of RNA extraction for different cells. We consider a simple
probabilistic model for read counts, based on a negative binomial distribution
for each gene, modified by a cell-dependent coefficient interpreted as an
extraction efficiency. We provide two alternative fast methods to estimate the
model parameters, together with the probability that a cell results in zero
read counts for a gene. This allows to measure genes co-expression and
differential expression in a novel way.
