Seurat: FeaturePlot – R documentation

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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,
  keep.scale = "feature",
  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 = NULL,
  interactive = FALSE,
  combine = TRUE,
  raster = NULL
)

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`
`keep.scale`	How to handle the color scale across multiple plots. Options are: "feature" (default; by row/feature scaling): The plots for each individual feature are scaled to the maximum expression of the feature across the conditions provided to 'split.by'. "all" (universal scaling): The plots for all features and conditions are scaled to the maximum expression value for the feature with the highest overall expression. NULL (no scaling): Each individual plot is scaled to the maximum expression value of the feature in the condition provided to 'split.by'. Be aware setting NULL will result in color scales that are not comparable between plots.
`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 `raster = FALSE`.
`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`	Redundant with `order`. This argument is being deprecated. Please use `order` instead.
`interactive`	Launch an interactive `FeaturePlot`
`combine`	Combine plots into a single `patchworked` ggplot object. If `FALSE`, return a list of ggplot objects
`raster`	Convert points to raster format, default is `NULL` which automatically rasterizes if plotting more than 100,000 cells

Value

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

Note

For the old do.hover and do.identify functionality, please see HoverLocator and CellSelector, respectively.

Examples

data("pbmc_small")
FeaturePlot(object = pbmc_small, features = 'PC_1')

Seurat

Tools for Single Cell Genomics

v4.0.1

GPL-3 | file LICENSE

Authors

Andrew Butler [ctb] (<https://orcid.org/0000-0003-3608-0463>), Saket Choudhary [ctb] (<https://orcid.org/0000-0001-5202-7633>), Charlotte Darby [ctb] (<https://orcid.org/0000-0003-2195-5300>), Jeff Farrell [ctb], Christoph Hafemeister [ctb] (<https://orcid.org/0000-0001-6365-8254>), Yuhan Hao [ctb] (<https://orcid.org/0000-0002-1810-0822>), Paul Hoffman [aut, cre] (<https://orcid.org/0000-0002-7693-8957>), Jaison Jain [ctb] (<https://orcid.org/0000-0002-9478-5018>), Efthymia Papalexi [ctb] (<https://orcid.org/0000-0001-5898-694X>), Patrick Roelli [ctb], Rahul Satija [ctb] (<https://orcid.org/0000-0001-9448-8833>), Karthik Shekhar [ctb], Avi Srivastava [ctb] (<https://orcid.org/0000-0001-9798-2079>), Tim Stuart [ctb] (<https://orcid.org/0000-0002-3044-0897>), Kristof Torkenczy [ctb] (<https://orcid.org/0000-0002-4869-7957>), Shiwei Zheng [ctb] (<https://orcid.org/0000-0001-6682-6743>), Satija Lab and Collaborators [fnd]

Initial release

2021-03-17