ezumap | R Documentation |
UMAP plot of first two dimensions using ggplot2.
ezumap(
object,
pheno.df,
name = "umap",
pca = TRUE,
initial_dims = nrow(pheno.df),
config = umap::umap.defaults,
method = c("naive", "umap-learn"),
preserve.seed = TRUE,
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 |
pca |
logical; Whether an initial PCA step should be performed (default: TRUE) |
initial_dims |
integer; the number of dimensions that should be retained in the initial PCA step (default: 50) |
config |
object of class umap.config |
method |
character, implementation. Available methods are 'naive' (an implementation written in pure R) and 'umap-learn' (requires python package 'umap-learn') |
preserve.seed |
logical, leave TRUE to insulate external code from randomness within the umap algorithms; set FALSE to allow randomness used in umap algorithms to alter the external random-number generator |
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? |
... |
list of settings with values overwrite defaults from UMAP |
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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