RNA velocity unraveled
Fig 9
The RNA velocity count processing and inference workflow, applied to data generated by stochastic simulation.
a. Schematic of the impulse model of gene modulation. b. Demonstration of the concordance between simulation and analytical solution for the occupation measure. i.: nascent mRNA counts; ii.: mature mRNA counts (gray: simulation; blue: occupation measure). c. Smoothing and imputation introduce distortions into the data. i.: raw data; ii.: data normalized to total counts; iii. imputed data (points: raw or processed observations; lines: ground truth averages μs and μu; red: spliced; yellow: unspliced). d. Local averages obtained by imputation are not interpretable as instantaneous averages. i.: mean unspliced; ii.: mean spliced; iii. variance unspliced; iv.: variance spliced (black points: true moment vs. pooled moment; blue line: identity; blue region: factors of ten around identity). e. Smoothing and imputation improve the inference on extrema. i.: moment-based inference from raw data; ii.: extremal inference from normalized data; iii.: extremal inference from imputed data (black points: true vs. inferred values of γ/β; blue line: identity; blue region: factors of ten around identity). The palette used is derived from dutchmasters by EdwinTh.