# Attach the DeLorean data frame to access members attach(dl)
The estimated pseudotime from the best sample against the observed capture time.
plot(dl, type="pseudotime")
The full tau posterior for each cell.
( ggplot(samples.l$tau %>% arrange(capture), aes(x=cell, y=tau, color=capture)) + geom_boxplot() + coord_flip() )
The posterior of the pseudotime offsets relative to the capture times. We also show the prior on the offset.
plot(dl, type="tau.offsets")
The posterior for the between time variation $\log \psi_g$:
(ggplot(samples.l$psi, aes(x=log(psi))) + geom_density() + geom_rug() + stat_function(fun=function(x) dnorm(x, mean=hyper$mu_psi, sd=hyper$sigma_psi), colour="blue", alpha=.7, linetype="dashed") )
Temporal variation by gene:
(ggplot(noise.levels, aes(x=gene, y=log10(psi), fill=gene %in% genes.high.psi), environment=environment()) + geom_boxplot() + coord_flip())
The posterior for the within time variation $\log \omega_g$:
(ggplot(samples.l$omega, aes(x=log(omega))) + geom_density() + geom_rug() + stat_function(fun=function(x) dnorm(x, mean=hyper$mu_omega, sd=hyper$sigma_omega), colour="blue", alpha=.7, linetype="dashed") )
Within time variation by gene:
(ggplot(noise.levels, aes(x=gene, y=log10(omega), fill=gene %in% genes.high.psi), environment=environment()) + geom_boxplot() + coord_flip())
The within time variation compared to the temporal variation (on log-log scale):
(ggplot(noise.levels, aes(x=log(psi), y=log(omega), color=gene %in% genes.high.psi), environment=environment()) + geom_point() + geom_abline(intercept=0, slope=1, linetype="dashed", alpha=.7))
have.cell.sizes <- 'S' %in% names(samples.l)
The posterior for the model log probability:
(ggplot(samples.l$lp__, aes(x=lp__)) + geom_density() + geom_rug())
# Detach the previously attached DeLorean data frame detach(dl)
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