Features correlation plot 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,
  slot = "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_scp",
  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: A character string specifying the column in the metadata to group cells by.
split.by: A character string specifying the column in the metadata to split the plot by.
cells: A character vector specifying the cells to include in the plot. If NULL (default), all cells will be included.
slot: A character string specifying the slot in the Seurat object to use. Defaults to "data".
assay: A character string specifying the assay to use. Defaults to the default assay in the Seurat object.
cor_method: A character string specifying the correlation method to use. Can be "pearson" (default) or "spearman".
adjust: A numeric value specifying the adjustment factor for the width of the violin plots. Defaults to 1.
margin: A numeric value specifying the margin size for the plot. Defaults to 1.
reverse: A logical value indicating whether to reverse the order of the features in the plot. Defaults to FALSE.
add_equation: A logical value indicating whether to add the equation of the linear regression line to each scatter plot. Defaults to FALSE.
add_r2: A logical value indicating whether to add the R-squared value of the linear regression line to each scatter plot. Defaults to TRUE.
add_pvalue: A logical value indicating whether to add the p-value of the linear regression line to each scatter plot. Defaults to TRUE.
add_smooth: A logical value indicating whether to add a smoothed line to each scatter plot. Defaults to TRUE.
palette: A character string specifying the name of the color palette to use for the groups. Defaults to "Paired".
palcolor: A character string specifying the color for the groups. Defaults to NULL.
cor_palette: A character string specifying the name of the color palette to use for the correlation. Defaults to "RuBu".
cor_palcolor: A character string specifying the color for the correlation. Defaults to "RuBu".
cor_range: A two-length numeric vector specifying the range for the correlation.
pt.size: A numeric value specifying the size of the points in the scatter plots. If NULL (default), the size will be automatically determined based on the number of cells.
pt.alpha: A numeric value between 0 and 1 specifying the transparency of the points in the scatter plots. Defaults to 1.
cells.highlight: A logical value or a character vector specifying the cells to highlight in the scatter plots. If TRUE, all cells will be highlighted. Defaults to NULL.
cols.highlight: A character string specifying the color for the highlighted cells. Defaults to "black".
sizes.highlight: A numeric value specifying the size of the highlighted cells in the scatter plots. Defaults to 1.
alpha.highlight: A numeric value between 0 and 1 specifying the transparency of the highlighted cells in the scatter plots. Defaults to 1.
stroke.highlight: A numeric value specifying the stroke size of the highlighted cells in the scatter plots. Defaults to 0.5.
calculate_coexp: A logical value indicating whether to calculate the co-expression of selected features. Defaults to FALSE.
raster: A logical value indicating whether to use raster graphics for scatter plots. Defaults to NULL.
raster.dpi: A numeric vector specifying the dpi (dots per inch) resolution for raster graphics in the scatter plots. Defaults to c(512, 512).
aspect.ratio: A numeric value specifying the aspect ratio of the scatter plots. Defaults to 1.
title: A character string specifying the title for the correlation plot. Defaults to NULL.
subtitle: A character string specifying the subtitle for the correlation plot. Defaults to NULL.
legend.position: A character string specifying the position of the legend. Can be "right" (default), "left", "top", or "bottom".
legend.direction: A character string specifying the direction of the legend. Can be "vertical" (default) or "horizontal".
theme_use: A character string specifying the name of the theme to use for the plot. Defaults to "theme_scp".
theme_args: A list of arguments to pass to the theme function. Defaults to an empty list.
combine: A logical value indicating whether to combine the plots into a single plot. Defaults to TRUE.
nrow: A numeric value specifying the number of rows in the combined plot. If NULL (default), the number of rows will be automatically determined.
ncol: A numeric value specifying the number of columns in the combined plot. If NULL (default), the number of columns will be automatically determined.
byrow: A logical value indicating whether to fill the combined plot byrow (top to bottom, left to right). Defaults to TRUE.
force: A logical value indicating whether to force the creation of the plot, even if it contains more than 50 subplots. Defaults to FALSE.
seed: A numeric value specifying the random seed for reproducibility. Defaults to 11.

Examples

data("pancreas_sub")
pancreas_sub <- Seurat::NormalizeData(pancreas_sub)
FeatureCorPlot(pancreas_sub, features = c("Neurog3", "Hes6", "Fev", "Neurod1", "Rbp4", "Pyy"), 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
)