posteriorCurvePlot: Posterior curve plot.
In kieranrcampbell/pseudogp: Probabilistic pseudotime for single-cell RNA-seq

Description Usage Arguments

View source: R/plotting.R

Flexible methods for plotting posterior mean curves or samples of posterior mean curves.

posteriorCurvePlot(X, fit, posterior_mean = TRUE, nsamples = 50, nnt = 80,
  point_colour = "darkred", curve_colour = "black", point_alpha = 1,
  curve_alpha = 0.5, grid_nrow = NULL, grid_ncol = NULL,
  use_cowplot = TRUE, standardize_ranges = FALSE)

`X`	The representation(s) passed to `fitPseudotime` (either a matrix or list of matrices)
`fit`	The `stanfit` object returned by `fitPseudotime`
`posterior_mean`	Logical. If TRUE (default) then the posterior mean curve is plotting at the MAP estimates of all inferred parameters. If FALSE, then `nsamples` will be randomly drawn from the posterior of the parameters and a curve plotted for each.
`nsamples`	The number of new points at which to calculate the posterior mean curves.
`nnt`	Number of new pseudo time points at which to plot the posterior mean curves.
`point_colour`	The colour of the points (cells) to draw
`curve_colour`	The colour of the curves to draw
`point_alpha`	The alpha (opacity) of the points
`curve_alpha`	The alpha (opacity) of the curves. Note that this is just a suggested value and the function will choose an appropriate value depending on the number of samples to plot. This is chosen as (1 - α) exp(1 - nsamples) + α*.
`grid_nrow`	If more than one representation is present then they're plotted in a grid. By default `cowplot` will choose the number of rows, but this overrides.
`grid_ncol`	If more than one representation is present then they're plotted in a grid. By default `cowplot` will choose the number of columns, but this overrides.
`use_cowplot`	Logical. Determines whether to use `theme_cowplot` or `theme_bw`.
`standardize_ranges`	Logical. If plotting multiple representations it can be useful to have x and y lims that don't depend on the fit (so plots align correctly). If this is set to FALSE, `ggplot2` calculates the x and y limits. If this is set to TRUE, the x and y limits are set to the minimum and maximum of the X values plus or minus 6% of the range between them.