Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information
Fig 5
Parameter inference and enrichment analysis for the cell cycle dataset.
The inference strategy involved in this figure is for kinetics/pulse data. A. Comparison of parameter inference results of our three stochastic models. From left to right are the comparison of γt of CSP-Baseline and CSP-Switching, the comparison of γt of CSP-Baseline and CSP-Splicing, the comparison of γt and γs in CSP-Splicing. The overlapping well-fitted genes were set as the overlap set of genes in the top 40% of the goodness-of-fit for both methods. B. Comparison of inferred parameters between our stochastic models and Dynamo’s method. Left: the comparison of γt between CSP-Baseline and Dynamo. Right: the comparison of β between CSP-Splicing and Dynamo. C. Comparison of the goodness-of-fit of the three stochastic models. Left: all highly variable genes. Right: genes in the top 10% of average new mRNA expression in highly variable genes. Here Base refers to the CSP-Baseline model, Splic to the CSP-Splicing model and Switch to the CSP-Switching model. D. Robust analysis. Left: Landscape of CSP-Baseline-based loss functions for the a typical gene WWTR1. Right: Scatter plot of robustness measure and goodness of fit for parameter inference. E. Enrichment analysis results of genes with high gene-wise γt, β (top 50%) in well fitted genes (top 40% of goodness of fit). F. Heat map of cell-wise parameters for well-fitted genes. From left to right, cell-wise α based on the CSP-Baseline, cell-wise αpon based on the CSP-Switching and cell-wise β based on the CSP-Splicing, respectively. Across all three heatmaps, the X-axis is the relative cell cycle position while the order of genes in the y-axis is arranged such that the peak time of each gene increases from the top left to bottom right.