Computes t-SNE into two dimensions and plots the map points.
In case there are missing values, PCA is performed using the nipals method
of pca
, the method can be changed to "ppca" if
nipals fails.
Usage
plot_tsne(
object,
all_features = FALSE,
center = TRUE,
scale = "uv",
perplexity = 30,
pca_method = "nipals",
color = NULL,
shape = color,
label = NULL,
density = FALSE,
title = "t-SNE",
subtitle = paste("Perplexity:", perplexity),
color_scale = NA,
shape_scale = getOption("notame.shape_scale"),
fill_scale = getOption("notame.fill_scale_dis"),
text_base_size = 14,
point_size = 2,
assay.type = NULL,
...
)
Arguments
- object
a
SummarizedExperiment
orMetaboSet
object- all_features
logical, should all features be used? If FALSE (the default), flagged features are removed before visualization.
- center
logical, should the data be centered prior to PCA? (usually yes)
- scale
scaling used, as in
prep
. Default is '"uv" for unit variance- perplexity
the perplexity used in t-SNE
- pca_method
the method used in PCA if there are missing values
- color
character, name of the column used for coloring the points. Set to NULL for black color.
- shape
character, name of the column used for shape. Set to NULL for uniform round shapes.
- label
character, name of the column used for point labels
- density
logical, whether to include density plots to both axes. The density curves will be split and colored by the 'color' variable.
- title, subtitle
the titles of the plot
- color_scale
the color scale as returned by a ggplot function. Set to NA to choose the appropriate scale based on the class of the coloring variable.
- shape_scale
the shape scale as returned by a ggplot function
- fill_scale
the fill scale used for density curves. If a continuous variable is used as color, density curve will be colorless.
- text_base_size
numeric, base size for text
- point_size
numeric, size of the points
- assay.type
character, assay to be used in case of multiple assays
- ...
additional arguments passed to
Rtsne
Value
A ggplot object. If density
is TRUE
, the plot will
consist of multiple parts and is harder to modify.
Examples
data(example_set)
plot_tsne(example_set, color = "Time", shape = "Group", perplexity = 10)