scater (version 1.0.4)

plotDiffusionMap: Plot a diffusion map for an SCESet object

Description

Produce a diffusion map plot of two components for an SCESet dataset.

Usage

plotDiffusionMap(object, ...)
plotDiffusionMapSCESet(object, ntop = 500, ncomponents = 2, exprs_values = "exprs", colour_by = NULL, shape_by = NULL, size_by = NULL, feature_set = NULL, return_SCESet = FALSE, scale_features = TRUE, draw_plot = TRUE, theme_size = 10, rand_seed = NULL, sigma = NULL, distance = "euclidean", legend = "auto", ...)
"plotDiffusionMap"(object, ntop = 500, ncomponents = 2, exprs_values = "exprs", colour_by = NULL, shape_by = NULL, size_by = NULL, feature_set = NULL, return_SCESet = FALSE, scale_features = FALSE, draw_plot = TRUE, theme_size = 10, rand_seed = NULL, sigma = NULL, distance = "euclidean", legend = "auto", ...)

Arguments

object

an SCESet object

...

further arguments passed to DiffusionMap

ntop

numeric scalar indicating the number of most variable features to use for the diffusion map. Default is 500, but any ntop argument is overrided if the feature_set argument is non-NULL.

ncomponents

numeric scalar indicating the number of principal components to plot, starting from the first diffusion map component. Default is 2. If ncomponents is 2, then a scatterplot of component 1 vs component 2 is produced. If ncomponents is greater than 2, a pairs plots for the top components is produced. NB: computing many components for the diffusion map can become time consuming.

exprs_values

character string indicating which values should be used as the expression values for this plot. Valid arguments are "tpm" (default; transcripts per million), "norm_tpm" (normalised TPM values), "fpkm" (FPKM values), "norm_fpkm" (normalised FPKM values), "counts" (counts for each feature), "norm_counts", "cpm" (counts-per-million), "norm_cpm" (normalised counts-per-million), "exprs" (whatever is in the 'exprs' slot of the SCESet object; default), "norm_exprs" (normalised expression values) or "stand_exprs" (standardised expression values) or any other named element of the assayData slot of the SCESet object that can be accessed with the get_exprs function.

colour_by

character string defining the column of pData(object) to be used as a factor by which to colour the points in the plot.

shape_by

character string defining the column of pData(object) to be used as a factor by which to define the shape of the points in the plot.

size_by

character string defining the column of pData(object) to be used as a factor by which to define the size of points in the plot.

feature_set

character, numeric or logical vector indicating a set of features to use for the diffusion map. If character, entries must all be in featureNames(object). If numeric, values are taken to be indices for features. If logical, vector is used to index features and should have length equal to nrow(object).

return_SCESet

logical, should the function return an SCESet object with principal component values for cells in the reducedDimension slot. Default is FALSE, in which case a ggplot object is returned.

scale_features

logical, should the expression values be standardised so that each feature has unit variance? Default is TRUE.

draw_plot

logical, should the plot be drawn on the current graphics device? Only used if return_SCESet is TRUE, otherwise the plot is always produced.

theme_size

numeric scalar giving default font size for plotting theme (default is 10).

rand_seed

(optional) numeric scalar that can be passed to set.seed to make plots reproducible.

sigma

argument passed to DiffusionMap

distance

argument passed to DiffusionMap

legend

character, specifying how the legend(s) be shown? Default is "auto", which hides legends that have only one level and shows others. Alternatives are "all" (show all legends) or "none" (hide all legends).

Value

If return_SCESet is TRUE, then the function returns an SCESet object, otherwise it returns a ggplot object.

Details

The function DiffusionMap is used internally to compute the diffusion map.

References

Haghverdi L, Buettner F, Theis FJ. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics. 2015; doi:10.1093/bioinformatics/btv325

See Also

destiny

Examples

## Set up an example SCESet
data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data = sc_example_cell_info)
example_sceset <- newSCESet(countData = sc_example_counts, phenoData = pd)
drop_genes <- apply(exprs(example_sceset), 1, function(x) {var(x) == 0})
example_sceset <- example_sceset[!drop_genes, ]

## Examples plotting diffusion maps
plotDiffusionMap(example_sceset)
plotDiffusionMap(example_sceset, colour_by = "Cell_Cycle")
plotDiffusionMap(example_sceset, colour_by = "Cell_Cycle",
shape_by = "Treatment")
plotDiffusionMap(example_sceset, colour_by = "Cell_Cycle",
shape_by = "Treatment", size_by = "Mutation_Status")
plotDiffusionMap(example_sceset, shape_by = "Treatment",
size_by = "Mutation_Status")
plotDiffusionMap(example_sceset, feature_set = 1:100, colour_by = "Treatment",
shape_by = "Mutation_Status")

plotDiffusionMap(example_sceset, shape_by = "Treatment",
return_SCESet = TRUE)