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Dimensional reduction plot for expression.

Source: R/SCP-plot.R
ExpDimPlot.Rd

Plotting cell points on a reduced 2D plane and coloring according to the expression of the features.

Usage

ExpDimPlot(
  srt,
  features,
  reduction = NULL,
  dims = c(1, 2),
  split.by = NULL,
  cells = NULL,
  slot = "data",
  assay = "RNA",
  show_stat = TRUE,
  palette = ifelse(isTRUE(compare_features), "Set1", "Spectral"),
  palcolor = NULL,
  pt.size = NULL,
  pt.alpha = 1,
  bg_cutoff = 0,
  bg_color = "grey80",
  keep_scale = NULL,
  lower_quantile = 0,
  upper_quantile = 0.99,
  lower_cutoff = NULL,
  upper_cutoff = NULL,
  add_density = FALSE,
  density_color = "grey80",
  density_filled = FALSE,
  density_filled_palette = "Greys",
  density_filled_color = NULL,
  cells.highlight = NULL,
  cols.highlight = "black",
  sizes.highlight = 1,
  alpha.highlight = 1,
  stroke.highlight = 0.5,
  calculate_coexp = FALSE,
  compare_features = FALSE,
  color_blend_mode = c("blend", "mix", "screen", "multiply"),
  label = FALSE,
  label.size = 4,
  label.fg = "white",
  label.bg = "black",
  label.bg.r = 0.1,
  label_insitu = FALSE,
  label_repel = FALSE,
  label_repulsion = 20,
  label_point_size = 1,
  label_point_color = "black",
  label_segment_color = "black",
  lineages = NULL,
  lineages_trim = c(0.01, 0.99),
  lineages_span = 0.75,
  lineages_palette = "Dark2",
  lineages_palcolor = NULL,
  lineages_arrow = arrow(length = unit(0.1, "inches")),
  lineages_line_size = 1,
  lineages_line_bg = "white",
  lineages_line_bg_r = 0.5,
  lineages_whiskers = FALSE,
  lineages_whiskers_size = 0.5,
  lineages_whiskers_alpha = 0.5,
  graph = NULL,
  edge_size = c(0.05, 0.5),
  edge_alpha = 0.1,
  edge_color = "grey40",
  hex = FALSE,
  hex.linewidth = 0.5,
  hex.color = "grey90",
  hex.bins = 50,
  hex.binwidth = NULL,
  raster = NULL,
  raster.dpi = c(512, 512),
  theme_use = "theme_scp",
  aspect.ratio = 1,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  lab_cex = 1,
  xlen_npc = 0.15,
  ylen_npc = 0.15,
  legend.position = "right",
  legend.direction = "vertical",
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  align = "hv",
  axis = "lr",
  force = FALSE
)

Arguments

srt

A Seurat object.

features

Name of one or more metadata columns to group (color) cells by (for example, orig.ident).

reduction

Which dimensionality reduction to use.

split.by

Name of a metadata column to split plot by.

palette

Name of palette to use. Default is "Paired".

pt.size

Point size for plotting.

pt.alpha

Point transparency.

bg_color

Color value for NA points.

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.

upper_cutoff
cells.highlight

A vector of names of cells to highlight.

cols.highlight

A vector of colors to highlight the cells.

sizes.highlight

Size of highlighted cells.

legend.position

The position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector)

legend.direction

Layout of items in legends ("horizontal" or "vertical")

combine

Whether to arrange multiple plots into a grid.

nrow

Number of rows in the plot grid.

ncol

Number of columns in the plot grid.

byrow

Logical value indicating if the plots should be arrange by row (default) or by column.

Value

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

Examples

data("pancreas_sub")
pancreas_sub <- Standard_SCP(pancreas_sub)
#> [2023-02-15 23:50:44] Start Standard_SCP
#> [2023-02-15 23:50:44] Checking srtList... ...
#> Data 1/1 of the srtList is raw_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 1/1 of the srtList...
#> Use the separate HVF from srtList...
#> Number of available HVF: 2000
#> [2023-02-15 23:50:45] Finished checking.
#> [2023-02-15 23:50:45] Perform ScaleData on the data...
#> [2023-02-15 23:50:46] Perform linear dimension reduction (pca) on the data...
#> [2023-02-15 23:50:47] Perform FindClusters (louvain) on the data...
#> [2023-02-15 23:50:47] Reorder clusters...
#> [2023-02-15 23:50:47] Perform nonlinear dimension reduction (umap) on the data...
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims_range:1-50) as input
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims_range:1-50) as input
#> [2023-02-15 23:50:58] Standard_SCP done
#> Elapsed time: 13.86 secs 
ExpDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP")

ExpDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP", hex = TRUE)
#> Warning: Removed 4 rows containing missing values (`geom_hex()`).
#> Warning: Removed 1 rows containing missing values (`geom_hex()`).


pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP")
#> Warning: Removed 8 rows containing missing values (`geom_path()`).
#> Warning: Removed 8 rows containing missing values (`geom_path()`).

ExpDimPlot(pancreas_sub, features = "Lineage3", reduction = "UMAP", lineages = "Lineage3")

ExpDimPlot(pancreas_sub, features = "Lineage3", reduction = "UMAP", lineages = "Lineage3", lineages_span = 0.1)


ExpDimPlot(pancreas_sub, c("Ins1", "Gcg", "Sst", "Ghrl"), reduction = "UMAP")

ExpDimPlot(pancreas_sub, c("Ins1", "Gcg", "Sst", "Ghrl"), reduction = "UMAP", lower_quantile = 0, upper_quantile = 0.8)

ExpDimPlot(pancreas_sub, c("Ins1", "Gcg", "Sst", "Ghrl"), reduction = "UMAP", lower_cutoff = 1, upper_cutoff = 4)

ExpDimPlot(pancreas_sub, c("Ins1", "Gcg", "Sst", "Ghrl"), reduction = "UMAP", bg_cutoff = 1, lower_cutoff = 1, upper_cutoff = 4)

ExpDimPlot(pancreas_sub, c("Ins1", "Gcg", "Sst", "Ghrl"), reduction = "UMAP", keep_scale = "all")

ExpDimPlot(pancreas_sub, c("Sst", "Ghrl"), split.by = "Phase", reduction = "UMAP", keep_scale = "feature")


ExpDimPlot(pancreas_sub,
  features = c("Ins1", "Gcg", "Sst", "Ghrl"), pt.size = 1,
  compare_features = TRUE, color_blend_mode = "blend",
  label = TRUE, label_insitu = TRUE
)

ExpDimPlot(pancreas_sub,
  features = c("S_score", "G2M_score"), pt.size = 1, palcolor = c("red", "green"),
  compare_features = TRUE, color_blend_mode = "mix", title = "mix",
  label = TRUE, label_insitu = TRUE
)

ExpDimPlot(pancreas_sub,
  features = c("S_score", "G2M_score"), pt.size = 1, palcolor = c("red", "green"),
  compare_features = TRUE, color_blend_mode = "blend", title = "blend",
  label = TRUE, label_insitu = TRUE
)

ExpDimPlot(pancreas_sub,
  features = c("S_score", "G2M_score"), pt.size = 1, palcolor = c("red", "green"),
  compare_features = TRUE, color_blend_mode = "screen", title = "screen",
  label = TRUE, label_insitu = TRUE
)

ExpDimPlot(pancreas_sub,
  features = c("S_score", "G2M_score"), pt.size = 1, palcolor = c("red", "green"),
  compare_features = TRUE, color_blend_mode = "multiply", title = "multiply",
  label = TRUE, label_insitu = TRUE
)


library(scales)
par(mfrow = c(2, 2))
show_col(c("red", "green", blendcolors(c("red", "green"), mode = "mix")), ncol = 4)
text(3.5, -0.5, "mix", cex = 1.2)
show_col(c("red", "green", blendcolors(c("red", "green"), mode = "blend")), ncol = 4)
text(3.5, -0.5, "blend", cex = 1.2)
show_col(c("red", "green", blendcolors(c("red", "green"), mode = "screen")), ncol = 4)
text(3.5, -0.5, "screen", cex = 1.2)
show_col(c("red", "green", blendcolors(c("red", "green"), mode = "multiply")), ncol = 4)
text(3.5, -0.5, "multiply", cex = 1.2)