plotSmoothers: Plot the log-transformed counts and the fitted values for a...

In statOmics/tradeSeq: trajectory-based differential expression analysis for sequencing data

Plot the log-transformed counts and the fitted values for a particular gene along all lineages

Description

Plot the smoothers estimated by tradeSeq.

Usage

plotSmoothers(models, ...)

## S4 method for signature 'gam'
plotSmoothers(
  models,
  nPoints = 100,
  lwd = 2,
  size = 2/3,
  xlab = "Pseudotime",
  ylab = "Log(expression + 1)",
  border = TRUE,
  alpha = 1,
  sample = 1
)

## S4 method for signature 'SingleCellExperiment'
plotSmoothers(
  models,
  counts,
  gene,
  nPoints = 100,
  lwd = 2,
  size = 2/3,
  xlab = "Pseudotime",
  ylab = "Log(expression + 1)",
  border = TRUE,
  alpha = 1,
  sample = 1,
  pointCol = NULL,
  curvesCols = NULL,
  plotLineages = TRUE,
  lineagesToPlot = NULL
)

Arguments

models	Either the SingleCellExperiment object obtained after running fitGAM, or the specific GAM model for the corresponding gene, if working with the list output of tradeSeq.
...	parameters including:
nPoints	The number of points used to extrapolate the fit. Defaults to 100.
lwd	Line width of the smoother. Passed to geom_line.
size	Character expansion of the data points. Passed to geom_point.
xlab	x-axis label. Passed to labs.
ylab	y-axis label. Passed to labs.
border	Logical: should a white border be drawn around the mean smoother.
alpha	Numeric between 0 and 1, determines the transparency of data points, see scale_color_viridis_d.
sample	Numeric between 0 and 1, use to subsample the cells when there are too many so that it can plot faster.
counts	The matrix of gene expression counts.
gene	Gene name or row in count matrix of gene to plot.
pointCol	Plotting colors for each cell. Can be either character vector of length 1, denoting a variable in the colData(models) to color cells by, or a vector of length equal to the number of cells.
curvesCols	Plotting colors for each curve Should be a list of colors of the exact same length as the number of curves, i.e. the number of lineages (if there is no conditions) or the number of lineages by the number of conditions. In the second case, the colors are grouped by condition (lineage 1 - condition 1, lineage 1 - condition 2,...).
plotLineages	Logical, should the mean smoothers for each lineage be plotted?
lineagesToPlot	A vector of integers referring to the lineages to be plotted.

Value

A ggplot object

Examples

set.seed(8)
data(crv, package="tradeSeq")
data(countMatrix, package="tradeSeq")
counts <- as.matrix(countMatrix)
sce <- fitGAM(counts = counts,
                  sds = crv,
                  nknots = 5)
plotSmoothers(sce, counts, rownames(counts)[1])
# Show only first lineage curve
curvesCols <- c("#440154FF", "transparent")
plotSmoothers(sce, counts, rownames(counts)[1], curvesCols = curvesCols,
  border = FALSE)
# Show only first curve and cells assigned to first lineage
plotSmoothers(sce, counts, rownames(counts)[1], curvesCols = curvesCols,
  border = FALSE) +
  ggplot2::scale_color_manual(values = curvesCols)

statOmics/tradeSeq documentation built on Jan. 19, 2024, 8:26 p.m.