plotTSNE: Plot t-SNE for an SCESet object

In dynverse/scaterlegacy: Single-cell analysis toolkit for gene expression data in R

Description

Produce a t-distributed stochastic neighbour embedding (t-SNE) plot of two components for an SCESet dataset.

Usage

plotTSNE(object, ...)

## S4 method for signature 'SCESet'
plotTSNE(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, perplexity = floor(ncol(object)/5), legend = "auto",
  ...)

Arguments

object	an SCESet object
...	further arguments passed to Rtsne
ntop	numeric scalar indicating the number of most variable features to use for the t-SNE Default is 500, but any ntop argument is overrided if the feature_set argument is non-NULL.
ncomponents	numeric scalar indicating the number of t-SNE components to plot, starting from the first t-SNE 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 more than two components for t-SNE can become very time consuming.
exprs_values	character string indicating which values should be used as the expression values for this plot. Valid arguments are "tpm" (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. Alternatively, a data frame with one column containing values to map to colours for all cells.
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. Alternatively, a data frame with one column containing values to map to shapes.
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. Alternatively, a data frame with one column containing values to map to sizes.
feature_set	character, numeric or logical vector indicating a set of features to use for the t-SNE calculation. 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.
perplexity	numeric scalar value defining the "perplexity parameter" for the t-SNE plot. Passed to Rtsne - see documentation for that package for more details.
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).

Details

The function Rtsne is used internally to compute the t-SNE.

Value

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

References

L.J.P. van der Maaten. Barnes-Hut-SNE. In Proceedings of the International Conference on Learning Representations, 2013.

See Also

Rtsne

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 PC1 and PC2
plotTSNE(example_sceset, perplexity = 10)
plotTSNE(example_sceset, colour_by = "Cell_Cycle", perplexity = 10)
plotTSNE(example_sceset, colour_by = "Cell_Cycle", shape_by = "Treatment",
perplexity = 10)
plotTSNE(example_sceset, colour_by = "Cell_Cycle", shape_by = "Treatment",
size_by = "Mutation_Status", perplexity = 10)
plotTSNE(example_sceset, shape_by = "Treatment", size_by = "Mutation_Status",
perplexity = 5)
plotTSNE(example_sceset, feature_set = 1:100, colour_by = "Treatment",
shape_by = "Mutation_Status", perplexity = 5)

plotTSNE(example_sceset, shape_by = "Treatment", return_SCESet = TRUE,
perplexity = 10)

dynverse/scaterlegacy documentation built on Feb. 17, 2020, 5:07 a.m.