Visualize RNA velocities on an existing embedding using correlation-based transition probability matrix within the kNN graph
show.velocity.on.embedding.cor(emb, vel, n = 100, cell.colors = NULL,
corr.sigma = 0.05, show.grid.flow = FALSE, grid.n = 20,
grid.sd = NULL, min.grid.cell.mass = 1, min.arrow.size = NULL,
arrow.scale = 1, max.grid.arrow.length = NULL,
fixed.arrow.length = FALSE, plot.grid.points = FALSE,
scale = "log", nPcs = NA, arrow.lwd = 1, xlab = "", ylab = "",
n.cores = defaultNCores(), do.par = T, show.cell = NULL,
cell.border.alpha = 0.3, cc = NULL, return.details = FALSE,
expression.scaling = FALSE, ...)embedding onto which to project the velocities; The dimensions of coordinates should be on the order of 10x10 for the default values to make sense.
velocity estimates (e.g. returned by gene.relative.velocity.estimates() )
neighborhood size (default=100 cells)
name vector of cell colors
sigma parameter used to translate velocity-(expression delta) correlation into a transition probability
whether to show grid velocity summary
number of grid points along each axis
standard deviation (in embedding coordinate space) used to determine the weighting of individual cells around each grid point
minimal cell "mass" (weighted number of cells) around each grid point required for it to show up
minimal arrow size
arrow scale multiplier
minimal arrow size
whether to use fixed arrow width (default=FALSE)
whether to mark all grid points with dots (even if they don't have valid velocities)
velocity scale to use (default: 'log', other values: 'sqrt','rank','linear')
number of PCs to use for velocity regularization (default NA, turns off regularization)
arrow width (under fixed.arrow.length=T)
x axis label
y axls label
number of cores to use
whether to reset plotting parameters
whether to show detailed velocity estimates for a specified cell
transparency for the cell border
velocity-(exprssion delta) correlation matrix (can be passed back from previous results, as $cc) to save calculation time when replotting the same velocity estimates on the same embedding with different parameters
whether to return detailed output (which can be passed to p1 app for visualization)
whether to scale the velocity length by the projection of velocity onto the expected expression change (based on the transition probability matrix)
extra parameters passed to plot() function
if return.details=F, returns invisible list containing transition probability matrix ($tp) and the velocity-(expression delta) correlation matrix ($cc). If return.details=T, returns a more extended list that can be passed as veloinfo to pagoda2::p2.make.pagoda1.app() for visualization