From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq

doi:10.1371/journal.pcbi.1014014

From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq

Fig 9

Analysis of mouse fibroblast scRNA-seq data reveals that many genes best fitted by the NB distribution are not transcriptionally bursty.

(a) Model selection using BIC on 21,684 genes (670 cells) after preprocessing shows that ∼80% of genes are best fitted by the NB model. (b,c) For these NB-fitted genes, Bayesian inference of was performed using the -modified telegraph model, with reliability assessed by the confidence interval criterion (CI range/median <0.4). The fraction of NB-fitted genes classified as transcriptionally bursty depends on the threshold chosen for : 0.1 in (b) and 0.25 in (c). In both cases, a substantial fraction of genes are best fitted by the NB model yet are not transcriptionally bursty.

doi: https://doi.org/10.1371/journal.pcbi.1014014.g009