Plot gene expression in reduced dimension.

Plot the gene in reduced dimensional space.

plotGeneCount(curve, ...)

# S4 method for SlingshotDataSet
plotGeneCount(
  curve,
  counts = NULL,
  gene = NULL,
  clusters = NULL,
  models = NULL,
  title = NULL
)

# S4 method for SingleCellExperiment
plotGeneCount(
  curve,
  counts = NULL,
  gene = NULL,
  clusters = NULL,
  models = NULL,
  title = NULL
)

# S4 method for CellDataSet
plotGeneCount(
  curve,
  counts = NULL,
  gene = NULL,
  clusters = NULL,
  models = NULL,
  title = NULL
)

Arguments

curve	One of three A SlingshotDataSet object. The output from trajectory inference using Slingshot. A SingleCellExperiment object. The output from trajectory inference using Slingshot. A CellDataset object.
...	parameters including:
counts	The count matrix, genes in rows and cells in columns. Only needed if the input is of the type SlingshotDataSet and the gene argument is not NULL.
gene	The name of gene for which you want to plot the count or the row number of that gene in the count matrix. Alternatively, one can specify the clusters argument.
clusters	The assignation of each cell to a cluster. Used to color the plot. Either clusters or gene and counts must be supplied.
models	The fitted GAMs, typically the output from fitGAM. Used to display the knots. Does not work with a CellDataset object as input.
title	Title for the plot.

Value

A ggplot object

Details

If both gene and clusters arguments are supplied, the plot will be colored according to gene count level. If none are provided, the function will fail. When a CellDataset object is provided as input, the function relies on the plot_cell_trajectory function

Examples

set.seed(97)
library(slingshot)
#> Loading required package: princurve
data(crv, package="tradeSeq")
data(countMatrix, package="tradeSeq")
rd <- slingshot::reducedDim(crv)
cl <- kmeans(rd, centers = 7)$cluster
lin <- slingshot::getLineages(rd, clusterLabels = cl, start.clus = 4)
#> Using full covariance matrix
crv <- slingshot::getCurves(lin)
counts <- as.matrix(countMatrix)
gamList <- fitGAM(counts = counts,
 pseudotime = slingPseudotime(crv, na = FALSE),
 cellWeights = slingCurveWeights(crv))
plotGeneCount(crv, counts, gene = "Mpo")