Fig. 9

Schematic showing several of the hidden (black) and observed (grey) data we model and their governing parameters. For this illustration, values were set as \(a=2\), \(b=25\) and \(\delta =0.001\) for the biological parameters and \(t=1000\), \(u\sim Pois(60)\), \(\lambda _n=0.075\), \(\lambda _s=0.01\) and \(\alpha \sim Beta(1,9)\) for the technical parameters. The encompassing boxes indicate the information used during parameter inference by model 1 (a and b) and 2 (a, b and \(\delta\)). The direction of the arrows indicate how the distributions feed into each other as dictated by the accompanying parameters. For example, a and b determine the steady state distribution, which determines the new and surviving transcript count distribution as dictated by \(\delta\) for given t, while the new and surviving T>C count distributions combine to form the observed T>C count distribution, which is conditional upon the cell’s transcript count, m, with \(m=100\) shown here. More information on estimating \(\alpha\) and \(\lambda _n\) specifically for the Qiu dataset is found in Additional file 1: Figs. S1 and S2, respectively