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Features correlation plot

Source: R/FeatureCorPlot.R
FeatureCorPlot.Rd

Pairwise feature correlations 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 = "Chinese",
  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,
  verbose = TRUE
)

Arguments

srt

A Seurat object.

features

Features to compare. Should be present in both the assay data and the metadata of the Seurat object.

group.by

Metadata column(s) used to color cells.

split.by

Metadata column to facet by.

cells

Cell names to include.

layer

Assay layer to use.

assay

Assay to use. NULL uses the default assay.

cor_method

"pearson" or "spearman".

adjust

The adjustment factor for the width of the violin plots.

margin

The margin size for the plot.

reverse

Whether to reverse the order of the features in the plot.

add_equation

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

add_r2

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

add_pvalue

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

add_smooth

Whether to add a smoothed line to each scatter plot.

palette, palcolor

Palette name (thisplot::show_palettes) or custom colors.

cor_palette

Name of the color palette to use for the correlation.

cor_palcolor

Color for the correlation.

cor_range

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

pt.size, pt.alpha

Point size and transparency. pt.size = NULL scales with sqrt(n) (minimum 0.3). Rasterized points keep at least a two-pixel radius at raster.dpi = c(512, 512) and scale with raster.dpi.

cells.highlight, cols.highlight, sizes.highlight, alpha.highlight, stroke.highlight

Cells to highlight and their appearance. TRUE highlights all cells.

calculate_coexp

Whether to calculate the co-expression of selected features.

raster, raster.dpi

Rasterize points. raster = NULL rasterizes when there are more than 100,000 cells.

aspect.ratio

Panel aspect ratio.

title

Plot title. NULL hides the title for merged/single panels. When multiple lineages are plotted and title is NULL, each panel is titled with its lineage column.

subtitle

Plot subtitle.

legend.position

Legend placement ("none", "left", "right", "bottom", "top"), direction, and title. legend.title = NULL uses the group name.

legend.direction

Legend direction: "horizontal" or "vertical".

theme_use, theme_args

Theme name or function, plus extra theme arguments.

combine, nrow, ncol, byrow

Combine plots with patchwork. combine = FALSE returns a list of ggplots.

force

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

seed

Random seed.

verbose

Whether to print messages.

See also

FeatureStatPlot

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 04:26:36] Start standard processing workflow...
#> ℹ [2026-08-30 04:26:36] Checking a list of <Seurat>...
#> ! [2026-08-30 04:26:36] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:26:36] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:26:36] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:26:36] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:26:36] Number of available HVF: 2000
#> ℹ [2026-08-30 04:26:36] Finished check
#> ℹ [2026-08-30 04:26:36] Perform `ScaleData()`
#> ℹ [2026-08-30 04:26:36] Perform pca linear dimension reduction
#> ℹ [2026-08-30 04:26:37] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 04:26:37] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 04:26:37] Reorder clusters...
#> ℹ [2026-08-30 04:26:37] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:26:37] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 04:26:45] Standard processing 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
)