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Features correlation plot

Source: R/FeatureCorPlot.R
FeatureCorPlot.Rd

This function creates a correlation plot to visualize the pairwise correlations between selected features in a Seurat object.

Usage

FeatureCorPlot(
  srt,
  features,
  group.by = NULL,
  split.by = NULL,
  cells = NULL,
  layer = "data",
  assay = NULL,
  cor_method = "pearson",
  adjust = 1,
  margin = 1,
  reverse = FALSE,
  add_equation = FALSE,
  add_r2 = TRUE,
  add_pvalue = TRUE,
  add_smooth = TRUE,
  palette = "Paired",
  palcolor = NULL,
  cor_palette = "RdBu",
  cor_palcolor = NULL,
  cor_range = c(-1, 1),
  pt.size = NULL,
  pt.alpha = 1,
  cells.highlight = NULL,
  cols.highlight = "black",
  sizes.highlight = 1,
  alpha.highlight = 1,
  stroke.highlight = 0.5,
  calculate_coexp = FALSE,
  raster = NULL,
  raster.dpi = c(512, 512),
  aspect.ratio = 1,
  title = NULL,
  subtitle = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  theme_use = "theme_scop",
  theme_args = list(),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  force = FALSE,
  seed = 11
)

Arguments

srt

A Seurat object.

features

A character vector specifying the features to compare. Should be present in both the assay data and the metadata of the Seurat object.

group.by

Name of one or more meta.data columns to group (color) cells by.

split.by

Name of a column in meta.data column to split plot by. Default is NULL.

cells

A character vector of cell names to use.

layer

Which layer to use. Default is data.

assay

Which assay to use. If NULL, the default assay of the Seurat object will be used.

cor_method

A character string specifying the correlation method to use. Can be "pearson" or "spearman". Default is "pearson".

adjust

The adjustment factor for the width of the violin plots. Default is 1.

margin

The margin size for the plot. Default is 1.

reverse

Whether to reverse the order of the features in the plot. Default is FALSE.

add_equation

Whether to add the equation of the linear regression line to each scatter plot. Default is FALSE.

add_r2

Whether to add the R-squared value of the linear regression line to each scatter plot. Default is TRUE.

add_pvalue

Whether to add the p-value of the linear regression line to each scatter plot. Default is TRUE.

add_smooth

Whether to add a smoothed line to each scatter plot. Default is TRUE.

palette

Color palette name. Available palettes can be found in thisplot::show_palettes. Default is "Paired".

palcolor

Custom colors used to create a color palette. Default is NULL.

cor_palette

A character string specifying the name of the color palette to use for the correlation. Default is "RdBu".

cor_palcolor

A character string specifying the color for the correlation. Default is "RdBu".

cor_range

A two-length numeric vector specifying the range for the correlation.

pt.size

The size of the points in the plot.

pt.alpha

The transparency of the data points. Default is 1.

cells.highlight

A logical or character vector specifying the cells to highlight in the plot. If TRUE, all cells are highlighted. If FALSE, no cells are highlighted. Default is NULL.

cols.highlight

Color used to highlight the cells.

sizes.highlight

Size of highlighted cell points.

alpha.highlight

Transparency of highlighted cell points.

stroke.highlight

Border width of highlighted cell points.

calculate_coexp

Whether to calculate the co-expression of selected features. Default is FALSE.

raster

Convert points to raster format. Default is NULL, which automatically rasterizes if plotting more than 100,000 cells.

raster.dpi

Pixel resolution for rasterized plots. Default is c(512, 512).

aspect.ratio

Aspect ratio of the panel. Default is 1.

title

The text for the title. Default is NULL.

subtitle

The text for the subtitle for the plot which will be displayed below the title. Default is NULL.

legend.position

The position of legends, one of "none", "left", "right", "bottom", "top". Default is "right".

legend.direction

The direction of the legend in the plot. Can be one of "vertical" or "horizontal".

theme_use

Theme used. Can be a character string or a theme function. Default is "theme_scop".

theme_args

Other arguments passed to the theme_use. Default is list().

combine

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

nrow

Number of rows in the combined plot. Default is NULL, which means determined automatically based on the number of plots.

ncol

Number of columns in the combined plot. Default is NULL, which means determined automatically based on the number of plots.

byrow

Whether to arrange the plots by row in the combined plot. Default is TRUE.

force

Whether to force the creation of the plot, even if it contains more than 50 subplots. Default is FALSE.

seed

Random seed for reproducibility. Default is 11.

See also

FeatureStatPlot

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 07:45:14] Start standard scop workflow...
#> ℹ [2026-01-27 07:45:15] Checking a list of <Seurat>...
#> ! [2026-01-27 07:45:15] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 07:45:15] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:45:17] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:45:17] Use the separate HVF from srt_list
#> ℹ [2026-01-27 07:45:17] Number of available HVF: 2000
#> ℹ [2026-01-27 07:45:17] Finished check
#> ℹ [2026-01-27 07:45:18] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 07:45:18] Perform pca linear dimension reduction
#> ℹ [2026-01-27 07:45:19] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 07:45:19] Reorder clusters...
#> ℹ [2026-01-27 07:45:19] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 07:45:19] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 07:45:22] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 07:45:26] Run scop standard workflow completed
FeatureCorPlot(
  pancreas_sub,
  features = rownames(pancreas_sub)[1:5],
  group.by = "SubCellType"
)


FeatureCorPlot(
  pancreas_sub,
  features = c(
    "nFeature_RNA",
    "nCount_RNA",
    "nFeature_spliced",
    "nCount_spliced",
    "nFeature_unspliced",
    "nCount_unspliced"
  ),
  group.by = "SubCellType",
  cor_palette = "Greys",
  cor_range = c(0, 1)
)
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable


FeatureCorPlot(
  pancreas_sub,
  features = c("nFeature_RNA", "nCount_RNA"),
  group.by = "SubCellType",
  add_equation = TRUE
)