Visualize feature values on a 2-dimensional reduction plot

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

Usage

FeatureDimPlot(
  srt,
  features,
  reduction = NULL,
  dims = c(1, 2),
  split.by = NULL,
  cells = NULL,
  slot = "data",
  assay = NULL,
  show_stat = ifelse(identical(theme_use, "theme_blank"), FALSE, TRUE),
  palette = ifelse(isTRUE(compare_features), "Set1", "Spectral"),
  palcolor = NULL,
  pt.size = NULL,
  pt.alpha = 1,
  bg_cutoff = 0,
  bg_color = "grey80",
  keep_scale = "feature",
  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_palcolor = 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", "average", "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_linewidth = 1,
  lineages_line_bg = "white",
  lineages_line_bg_stroke = 0.5,
  lineages_whiskers = FALSE,
  lineages_whiskers_linewidth = 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),
  aspect.ratio = 1,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = 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 or a named list of features to plot. Features can be gene names in Assay or names of numeric columns in meta.data.

reduction

Which dimensionality reduction to use. If not specified, will use the reduction returned by DefaultReduction.

dims

Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions.

split.by

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

cells

Subset cells to plot.

slot

Which slot to pull expression data from? Default is data.

assay

Which assay to pull expression data from. If NULL, will use the assay returned by DefaultAssay.

show_stat

Whether to show statistical information on the plot.

palette

Name of a color palette name collected in SCP.

palcolor

Custom colors used to create a color palette.

pt.size

Point size for plotting.

pt.alpha

Point transparency.

bg_cutoff

Background cutoff. Points with feature values lower than the cutoff will be considered as background and will be colored with bg_color.

bg_color

Color value for background points.

keep_scale

How to handle the color scale across multiple plots. Options are:

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.
"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.

lower_quantile, upper_quantile, lower_cutoff, upper_cutoff

Vector of minimum and maximum cutoff values or quantile values for each feature.

add_density

Whether to add a density layer on the plot.

density_color

Color of the density contours lines.

density_filled

Whether to add filled contour bands instead of contour lines.

density_filled_palette

Color palette used to fill contour bands.

density_filled_palcolor

Custom colors used to fill contour bands.

cells.highlight

A vector of cell names to highlight.

cols.highlight

Color used to highlight the cells.

sizes.highlight

Size of highlighted cells.

alpha.highlight

Transparency of highlighted cell points.

stroke.highlight

Border width of highlighted cell points.

calculate_coexp

Whether to calculate the co-expression value (geometric mean) of the features.

compare_features

Whether to show the values of multiple features on a single plot.

color_blend_mode

Blend mode to use when compare_features = TRUE

label

Whether the feature name is labeled in the center of the location of cells wieh high expression.

label.size

Size of labels.

label.fg

Foreground color of label.

label.bg

Background color of label.

label.bg.r

Background ratio of label.

label_insitu

Whether the labels is feature names instead of numbers. Valid only when compare_features = TRUE.

label_repel

Logical value indicating whether the label is repel away from the center location.

label_repulsion

Force of repulsion between overlapping text labels. Defaults to 20.

label_point_size

Size of the center points.

label_point_color

Color of the center points

label_segment_color

Color of the line segment for labels.

lineages

Lineages/pseudotime to add to the plot. If specified, curves will be fitted using loess method.

lineages_trim

Trim the leading and the trailing data in the lineages.

lineages_span

The parameter α which controls the degree of smoothing in loess method.

lineages_palette

Color palette used for lineages.

lineages_palcolor

Custom colors used for lineages.

lineages_arrow

Set arrows of the lineages. See arrow.

lineages_linewidth

Width of fitted curve lines for lineages.

lineages_line_bg

Background color of curve lines for lineages.

lineages_line_bg_stroke

Border width of curve lines background.

lineages_whiskers

Whether to add whiskers for lineages.

lineages_whiskers_linewidth

Width of whiskers for lineages.

lineages_whiskers_alpha

Transparency of whiskers for lineages.

graph

Specify the graph name to add edges between cell neighbors to the plot.

edge_size

Size of edges.

edge_alpha

Transparency of edges.

edge_color

Color of edges.

hex

Whether to chane the plot type from point to the hexagonal bin.

hex.linewidth

Border width of hexagonal bins.

hex.color

Border color of hexagonal bins.

hex.bins

Number of hexagonal bins.

hex.binwidth

Hexagonal bin width.

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, passed to geom_scattermore(). Default is c(512, 512).

aspect.ratio

Aspect ratio of the panel.

title

The text for the title.

subtitle

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

xlab

x-axis label.

ylab

y-axis label.

legend.position

The position of legends ("none", "left", "right", "bottom", "top").

legend.direction

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

theme_use

Theme used. Can be a character string or a theme function. For example, "theme_blank" or ggplot2::theme_classic.

theme_args

Other arguments passed to the theme_use.

combine

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

nrow

Number of rows in the combined plot.

ncol

Number of columns in the combined plot.

byrow

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

force

Whether to force drawing regardless of the number of features greater than 100.

seed

Random seed set for reproducibility

Examples

data("pancreas_sub")
FeatureDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP")

FeatureDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP", bg_cutoff = -Inf)

FeatureDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP", theme_use = "theme_blank")

FeatureDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP", theme_use = ggplot2::theme_classic, theme_args = list(base_size = 16))

FeatureDimPlot(pancreas_sub, features = "G2M_score", reduction = "UMAP") %>% panel_fix(height = 2, raster = TRUE, dpi = 30)


pancreas_sub <- Standard_SCP(pancreas_sub)
#> [2023-11-21 07:15:14.873889] Start Standard_SCP
#> [2023-11-21 07:15:14.874034] 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
#> [2023-11-21 07:15:15.484776] Finished checking.
#> [2023-11-21 07:15:15.484961] Perform ScaleData on the data...
#> [2023-11-21 07:15:15.684053] Perform linear dimension reduction (pca) on the data...
#> Warning: The following arguments are not used: force.recalc
#> Warning: The following arguments are not used: force.recalc
#> [2023-11-21 07:15:16.296121] Perform FindClusters (louvain) on the data...
#> [2023-11-21 07:15:16.370625] Reorder clusters...
#> [2023-11-21 07:15:16.436033] Perform nonlinear dimension reduction (umap) on the data...
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-13) as input
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-13) as input
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> [2023-11-21 07:15:23.868927] Standard_SCP done
#> Elapsed time: 9 secs 
FeatureDimPlot(pancreas_sub, features = c("StandardPC_1", "StandardPC_2"), reduction = "UMAP", bg_cutoff = -Inf)


# Label and highlight cell points
FeatureDimPlot(pancreas_sub,
  features = "Rbp4", reduction = "UMAP", label = TRUE,
  cells.highlight = colnames(pancreas_sub)[pancreas_sub$SubCellType == "Delta"]
)

FeatureDimPlot(pancreas_sub,
  features = "Rbp4", split.by = "Phase", reduction = "UMAP",
  cells.highlight = TRUE, theme_use = "theme_blank"
)


# Add a density layer
FeatureDimPlot(pancreas_sub, features = "Rbp4", reduction = "UMAP", label = TRUE, add_density = TRUE)

FeatureDimPlot(pancreas_sub, features = "Rbp4", reduction = "UMAP", label = TRUE, add_density = TRUE, density_filled = TRUE)
#> Warning: Removed 396 rows containing missing values (`geom_raster()`).


# Chane the plot type from point to the hexagonal bin
FeatureDimPlot(pancreas_sub, features = "Rbp4", reduction = "UMAP", hex = TRUE)
#> Warning: Removed 4 rows containing missing values (`geom_hex()`).

FeatureDimPlot(pancreas_sub, features = "Rbp4", reduction = "UMAP", hex = TRUE, hex.bins = 20)
#> Warning: Removed 3 rows containing missing values (`geom_hex()`).
#> Warning: Removed 5 rows containing missing values (`geom_hex()`).


# Show lineages on the plot based on the pseudotime
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()`).

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

FeatureDimPlot(pancreas_sub, features = "Lineage3", reduction = "UMAP", lineages = "Lineage3", lineages_whiskers = TRUE)

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


# Input a named feature list
markers <- list(
  "Ductal" = c("Sox9", "Anxa2", "Bicc1"),
  "EPs" = c("Neurog3", "Hes6"),
  "Pre-endocrine" = c("Fev", "Neurod1"),
  "Endocrine" = c("Rbp4", "Pyy"),
  "Beta" = "Ins1", "Alpha" = "Gcg", "Delta" = "Sst", "Epsilon" = "Ghrl"
)
FeatureDimPlot(pancreas_sub,
  features = markers, reduction = "UMAP",
  theme_use = "theme_blank",
  theme_args = list(plot.subtitle = ggplot2::element_text(size = 10), strip.text = ggplot2::element_text(size = 8))
)


# Plot multiple features with different scales
endocrine_markers <- c("Beta" = "Ins1", "Alpha" = "Gcg", "Delta" = "Sst", "Epsilon" = "Ghrl")
FeatureDimPlot(pancreas_sub, endocrine_markers, reduction = "UMAP")

FeatureDimPlot(pancreas_sub, endocrine_markers, reduction = "UMAP", lower_quantile = 0, upper_quantile = 0.8)

FeatureDimPlot(pancreas_sub, endocrine_markers, reduction = "UMAP", lower_cutoff = 1, upper_cutoff = 4)

FeatureDimPlot(pancreas_sub, endocrine_markers, reduction = "UMAP", bg_cutoff = 2, lower_cutoff = 2, upper_cutoff = 4)

FeatureDimPlot(pancreas_sub, endocrine_markers, reduction = "UMAP", keep_scale = "all")

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


# Plot multiple features on one picture
FeatureDimPlot(pancreas_sub,
  features = endocrine_markers, pt.size = 1,
  compare_features = TRUE, color_blend_mode = "blend",
  label = TRUE, label_insitu = TRUE
)

FeatureDimPlot(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
)

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

FeatureDimPlot(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
)

FeatureDimPlot(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
)

Visualize feature values on a 2-dimensional reduction plot

Usage

Arguments

See also

Examples