Plot the smoothers estimated by
plotSmoothers(models, ...) # S4 method for 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 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 )
The number of points used to extrapolate the fit. Defaults to 100.
Line width of the smoother. Passed to
Character expansion of the data points. Passed to
x-axis label. Passed to
y-axis label. Passed to
Logical: should a white border be drawn around the mean smoother.
Numeric between 0 and 1, determines the transparency of data points,
Numeric between 0 and 1, use to subsample the cells when there are too many so that it can plot faster.
The matrix of gene expression counts.
Gene name or row in count matrix of gene to plot.
Plotting colors for each cell. Can be either character vector of
length 1, denoting a variable in the
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,...).
Logical, should the mean smoothers for each lineage be plotted?
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))# Show only first lineage curve curvesCols <- c("#440154FF", "transparent") plotSmoothers(sce, counts, rownames(counts), curvesCols = curvesCols, border = FALSE)# Show only first curve and cells assigned to first lineage plotSmoothers(sce, counts, rownames(counts), curvesCols = curvesCols, border = FALSE) + ggplot2::scale_color_manual(values = curvesCols)#>#>