eztsne | R Documentation |
tSNE plot of first two dimensions using ggplot2.
eztsne(
object,
pheno.df,
name = "tsne",
check_duplicates = FALSE,
pca = TRUE,
perplexity = min(30, round(ncol(object)/5)),
theta = 0.5,
seed = 123,
initial_dims = 50,
max_iter = 1000,
alpha = 1,
all.size = NULL,
facet = NULL,
title = NULL,
subtitle = NULL,
rm.leg.title = FALSE,
labels = FALSE,
manual.color = NULL,
manual.shape = NULL,
plot = TRUE,
...
)
object |
Matrix-like object with features (e.g. genes) as rows and samples as columns. |
pheno.df |
Data frame with rows as samples and columns as phenotypes. |
name |
Name of file to create. Set to |
check_duplicates |
logical; Checks whether duplicates are present. It is best to make sure there are no duplicates present and set this option to FALSE, especially for large datasets (default: TRUE) |
pca |
logical; Whether an initial PCA step should be performed (default: TRUE) |
perplexity |
numeric; Perplexity parameter (should not be bigger than 3 * perplexity < nrow(X) - 1, see details for interpretation) |
theta |
numeric; Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE (default: 0.5) |
seed |
Integer seed to set for reproducility if |
initial_dims |
integer; the number of dimensions that should be retained in the initial PCA step (default: 50) |
max_iter |
integer; Number of iterations (default: 1000) |
alpha |
Transparency, passed to |
all.size |
Passed to |
facet |
A formula with columns in |
title |
Title text; suppressed if it is |
subtitle |
Subtitle text; suppressed if it is |
rm.leg.title |
Logical indicating if legend title should be removed. |
labels |
Logical, should sample labels be added next to points? |
manual.color |
Vector passed to |
manual.shape |
Vector passed to |
plot |
Logical; should plot be generated? |
... |
Passed to |
object
must have colnames, and if pheno.df
is given, it is checked that colnames(object)==rownames(pheno.df)
.
Invisibly, a ggplot
object. Its data
element contains the first two principal components
appended to pheno.df
.
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