Seurat (version 3.1.4)

FeaturePlot: Visualize 'features' on a dimensional reduction plot

Description

Colors single cells on a dimensional reduction plot according to a 'feature' (i.e. gene expression, PC scores, number of genes detected, etc.)

Usage

FeaturePlot(
  object,
  features,
  dims = c(1, 2),
  cells = NULL,
  cols = if (blend) {     c("lightgrey", "#ff0000", "#00ff00") } else {    
    c("lightgrey", "blue") },
  pt.size = NULL,
  order = FALSE,
  min.cutoff = NA,
  max.cutoff = NA,
  reduction = NULL,
  split.by = NULL,
  shape.by = NULL,
  slot = "data",
  blend = FALSE,
  blend.threshold = 0.5,
  label = FALSE,
  label.size = 4,
  repel = FALSE,
  ncol = NULL,
  coord.fixed = FALSE,
  by.col = TRUE,
  sort.cell = FALSE,
  combine = TRUE
)

Arguments

object

Seurat object

features

Vector of features to plot. Features can come from:

• An Assay feature (e.g. a gene name - "MS4A1")

• A column name from meta.data (e.g. mitochondrial percentage - "percent.mito")

• A column name from a DimReduc object corresponding to the cell embedding values (e.g. the PC 1 scores - "PC_1")

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 TRUE, takes anywhere from 1-3 colors:

1 color:

Treated as color for double-negatives, will use default colors 2 and 3 for per-feature expression

2 colors:

Treated as colors for per-feature expression, will use default color 1 for double-negatives

3+ colors:

First color used for double-negatives, colors 2 and 3 used for per-feature expression, all others ignored

pt.size

Adjust point size for plotting

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 feature plot by, pass 'ident' to split by cell identity'; similar to the old FeatureHeatmap

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

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

repel

Repel labels

ncol

Number of columns to combine multiple feature plots to, ignored if split.by is not NULL

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 blend = TRUE

sort.cell

If TRUE, the positive cells will overlap the negative cells

combine

Combine plots into a single patchworked ggplot object. If FALSE, return a list of ggplot objects

Value

A patchworked ggplot object if combine = TRUE; otherwise, a list of ggplot objects

See Also

DimPlot HoverLocator CellSelector

Examples

# NOT RUN {
FeaturePlot(object = pbmc_small, features = 'PC_1')

# }