View source: R/visualization.R
FeaturePlot | R Documentation |
Colors single cells on a dimensional reduction plot according to a 'feature' (i.e. gene expression, PC scores, number of genes detected, etc.)
FeaturePlot(
object,
features,
dims = c(1, 2),
cells = NULL,
cols = if (blend) {
c("lightgrey", "#ff0000", "#00ff00")
} else {
c("lightgrey", "blue")
},
pt.size = NULL,
alpha = 1,
order = FALSE,
min.cutoff = NA,
max.cutoff = NA,
reduction = NULL,
split.by = NULL,
keep.scale = "feature",
shape.by = NULL,
slot = "data",
blend = FALSE,
blend.threshold = 0.5,
label = FALSE,
label.size = 4,
label.color = "black",
repel = FALSE,
ncol = NULL,
coord.fixed = FALSE,
by.col = TRUE,
sort.cell = deprecated(),
interactive = FALSE,
combine = TRUE,
raster = NULL,
raster.dpi = c(512, 512)
)
object |
Seurat object |
features |
Vector of features to plot. Features can come from:
|
dims |
Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions |
cells |
Vector of cells to plot (default is all cells) |
cols |
The two colors to form the gradient over. Provide as string vector with
the first color corresponding to low values, the second to high. Also accepts a Brewer
color scale or vector of colors. Note: this will bin the data into number of colors provided.
When blend is
|
pt.size |
Adjust point size for plotting |
alpha |
Alpha value for plotting (default is 1) |
order |
Boolean determining whether to plot cells in order of expression. Can be useful if cells expressing given feature are getting buried. |
min.cutoff , max.cutoff |
Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10') |
reduction |
Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca |
split.by |
A factor in object metadata to split the plot by, pass 'ident' to split by cell identity' |
keep.scale |
How to handle the color scale across multiple plots. Options are:
|
shape.by |
If NULL, all points are circles (default). You can specify any
cell attribute (that can be pulled with FetchData) allowing for both
different colors and different shapes on cells. Only applicable if |
slot |
Which slot to pull expression data from? |
blend |
Scale and blend expression values to visualize coexpression of two features |
blend.threshold |
The color cutoff from weak signal to strong signal; ranges from 0 to 1. |
label |
Whether to label the clusters |
label.size |
Sets size of labels |
label.color |
Sets the color of the label text |
repel |
Repel labels |
ncol |
Number of columns to combine multiple feature plots to, ignored if |
coord.fixed |
Plot cartesian coordinates with fixed aspect ratio |
by.col |
If splitting by a factor, plot the splits per column with the features as rows; ignored if |
sort.cell |
Redundant with |
interactive |
Launch an interactive |
combine |
Combine plots into a single |
raster |
Convert points to raster format, default is |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
For the old do.hover
and do.identify
functionality, please see
HoverLocator
and CellSelector
, respectively.
DimPlot
HoverLocator
CellSelector
data("pbmc_small")
FeaturePlot(object = pbmc_small, features = 'PC_1')
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