Extended Data Fig. 1: Robustness analysis: transcription rate.
From: Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
Shown are posterior distributions over: production rates βl, and transition rates \({k}_{{\sigma }_{l}\to {\sigma }_{{l}^{{\prime} }}}\). Across columns, the breadth of the distributions is comparable for a model containing two gene states, under various maximum ground-truth production rates. Again, the posterior maximum closely matches the ground truth, demonstrating the method’s robustness under quantitative changes in RNA count distribution. As before, each data point was generated using the Gillespie stochastic simulation algorithm, with weak limit set to L = 8 (as per Fig. 2). Rates in each column are inferred for 600 cells observed per time point with 20 collection times at [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 120, 180, 240, 360, 480, 600, 1200, 3600] (s).
