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2D-Dimensional reduction plot for classification.

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

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

Usage

ClassDimPlot(
  srt,
  group.by = "orig.ident",
  reduction = NULL,
  dims = c(1, 2),
  split.by = NULL,
  cells = NULL,
  show_na = FALSE,
  show_stat = TRUE,
  pt.size = NULL,
  pt.alpha = 1,
  palette = "Paired",
  palcolor = NULL,
  bg_color = "grey80",
  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",
  cells.highlight = NULL,
  cols.highlight = "black",
  sizes.highlight = 1,
  alpha.highlight = 1,
  stroke.highlight = 0.5,
  add_density = FALSE,
  density_color = "grey80",
  density_filled = FALSE,
  density_filled_palette = "Greys",
  density_filled_color = NULL,
  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,
  stat.by = NULL,
  stat_type = "percent",
  stat_plot_type = "pie",
  stat_plot_position = c("stack", "dodge"),
  stat_plot_size = 0.1,
  stat_plot_palette = "Set1",
  stat_palcolor = NULL,
  stat_plot_alpha = 1,
  stat_plot_label = FALSE,
  stat_plot_label_size = 3,
  graph = NULL,
  edge_size = c(0.05, 0.5),
  edge_alpha = 0.1,
  edge_color = "grey40",
  paga = NULL,
  paga_type = "connectivities",
  paga_node_size = 4,
  paga_edge_threshold = 0.01,
  paga_edge_size = c(0.2, 1),
  paga_edge_color = "grey40",
  paga_edge_alpha = 0.5,
  paga_transition_threshold = 0.01,
  paga_transition_size = c(0.2, 1),
  paga_transition_color = "black",
  paga_transition_alpha = 1,
  paga_show_transition = FALSE,
  velocity = NULL,
  velocity_plot_type = "raw",
  velocity_n_neighbors = ceiling(ncol(srt)/50),
  velocity_density = 1,
  velocity_smooth = 0.5,
  velocity_scale = 1,
  velocity_min_mass = 1,
  velocity_cutoff_perc = 5,
  velocity_arrow_color = "black",
  velocity_arrow_angle = 20,
  velocity_arrow_flank = 0.8,
  streamline_L = 5,
  streamline_minL = 1,
  streamline_res = 1,
  streamline_n = 15,
  streamline_jitter = 1,
  streamline_size = c(0, 0.8),
  streamline_alpha = 1,
  streamline_color = NULL,
  streamline_palette = "RdYlBu",
  streamline_palcolor = NULL,
  streamline_bg_color = "white",
  streamline_bg_stroke = 0.5,
  hex = FALSE,
  hex.linewidth = 0.5,
  hex.count = TRUE,
  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.

group.by

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.

cells
pt.size

Point size for plotting.

pt.alpha

Point transparency.

palette

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

bg_color

Color value for NA points.

label

Whether to label the groups.

label.size

Plot resolution. Only valid when units is not px

label_insitu

Whether place the raw label text in the original position. Default is FALSE, which using numbers instead of raw labels.

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")
ClassDimPlot(pancreas_sub,
  group.by = "CellType", reduction = "UMAP", label = TRUE, add_density = TRUE,
  cells.highlight = colnames(pancreas_sub)[pancreas_sub$SubCellType == "Delta"]
)

ClassDimPlot(pancreas_sub,
  group.by = "SubCellType", split.by = "Phase", cells.highlight = TRUE,
  theme_use = "theme_blank", legend.position = "none"
)


# Add statistical chart
ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", stat.by = "Phase")

ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", stat.by = "Phase", stat_plot_type = "ring", stat_plot_label = TRUE, stat_plot_size = 0.15)
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).
#> Warning: Removed 1 rows containing missing values (`position_stack()`).

ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", stat.by = "Phase", stat_type = "count", stat_plot_type = "bar", stat_plot_position = "dodge")


# Chane the plot type from point to the hexagonal bin
ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", hex = TRUE)
#> Warning: Removed 5 rows containing missing values (`geom_hex()`).

ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", hex = TRUE, hex.bins = 20)
#> Warning: Removed 4 rows containing missing values (`geom_hex()`).

ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", hex = TRUE, hex.count = FALSE)
#> Warning: Removed 5 rows containing missing values (`geom_hex()`).


# Show cell-cell graph on the plot
pancreas_sub <- Standard_SCP(pancreas_sub)
#> [2023-02-15 23:40:48] Start Standard_SCP
#> [2023-02-15 23:40:48] 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 23:40:49] Finished checking.
#> [2023-02-15 23:40:49] Perform ScaleData on the data...
#> [2023-02-15 23:40:49] Perform linear dimension reduction (pca) on the data...
#> [2023-02-15 23:40:50] Perform FindClusters (louvain) on the data...
#> [2023-02-15 23:40:50] Reorder clusters...
#> [2023-02-15 23:40:51] 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 23:41:00] Standard_SCP done
#> Elapsed time: 12.06 secs 
ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", graph = "Standardpca_SNN")

ClassDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", graph = "Standardpca_SNN", edge_color = "grey80")


# Show the lineage on the plot based on the pseudotime
pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", show_plot = FALSE)
ExpDimPlot(pancreas_sub, features = paste0("Lineage", 1:3), reduction = "UMAP")

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:3))
#> Warning: Removed 8 rows containing missing values (`geom_path()`).
#> Warning: Removed 8 rows containing missing values (`geom_path()`).

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:3), lineages_whiskers = TRUE)
#> Warning: Removed 8 rows containing missing values (`geom_segment()`).
#> Warning: Removed 8 rows containing missing values (`geom_path()`).
#> Warning: Removed 8 rows containing missing values (`geom_path()`).

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:3), lineages_span = 0.1)


# Show PAGA result on the plot
pancreas_sub <- RunPAGA(srt = pancreas_sub, group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP", return_seurat = TRUE)
#> Warning: SCP_env python environment does not exist. Create it with the PrepareEnv function...
#> + '/usr/share/miniconda/bin/conda' 'create' '--yes' '--name' 'SCP_env' 'python=3.8' '--quiet' '-c' 'conda-forge'
#> Try to install numpy==1.21.6,numba==0.55.2,scikit-learn==1.1.2,pandas==1.3.5,python-igraph==0.10.2,matplotlib==3.6.3,scipy,versioned-hdf5,leidenalg,scanpy,scvelo,palantir ...
#> ====================== SCP conda environment ======================
#> conda: /usr/share/miniconda/bin/conda
#> environment: /usr/share/miniconda/envs/SCP_env
#> ======================== SCP python config ========================
#> python:         /usr/share/miniconda/envs/SCP_env/bin/python3.8
#> libpython:      /usr/share/miniconda/envs/SCP_env/lib/libpython3.8.so
#> pythonhome:     /usr/share/miniconda/envs/SCP_env:/usr/share/miniconda/envs/SCP_env
#> version:        3.8.16 | packaged by conda-forge | (default, Feb  1 2023, 16:01:55)  [GCC 11.3.0]
#> numpy:          /usr/share/miniconda/envs/SCP_env/lib/python3.8/site-packages/numpy
#> numpy_version:  1.21.6
#> 
#> NOTE: Python version was forced by use_python function
#> ===================================================================
#> 'misc' slot is not converted.
#> 'tools' slot is not converted.
#> Warning: 'uns: neighbors' will not be converted. You may need to convert it manually.
ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", paga = pancreas_sub@misc$paga)

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", paga = pancreas_sub@misc$paga, paga_type = "connectivities_tree")

ClassDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2,
  label = TRUE, label_repel = TRUE, label_insitu = TRUE, label_segment_color = "transparent",
  paga = pancreas_sub@misc$paga, paga_edge_threshold = 0.1, paga_edge_color = "black", paga_edge_alpha = 1,
  show_stat = FALSE, legend.position = "none", theme_use = "theme_blank"
)


# Show RNA velocity result on the plot
pancreas_sub <- RunSCVELO(srt = pancreas_sub, group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP", mode = "stochastic", return_seurat = TRUE)
#> 'misc' slot is not converted.
#> 'tools' slot is not converted.
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from variance_stochastic_ to variancestochastic_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from variance_velocity_ to variancevelocity_
#> Warning: 'uns: neighbors' will not be converted. You may need to convert it manually.
ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", paga = pancreas_sub@misc$paga, paga_show_transition = TRUE)

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = NA, velocity = "stochastic")
#> Warning: Removed 1000 rows containing missing values (`geom_point()`).
#> Warning: Removed 1000 rows containing missing values (`geom_point()`).
#> Warning: Removed 4 rows containing missing values (`geom_segment()`).

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2, velocity = "stochastic", velocity_plot_type = "grid")
#> Warning: Removed 14 rows containing missing values (`geom_segment()`).
#> Warning: Removed 14 rows containing missing values (`geom_segment()`).

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2, velocity = "stochastic", velocity_plot_type = "grid", velocity_scale = 1.5)
#> Warning: Removed 14 rows containing missing values (`geom_segment()`).
#> Warning: Removed 14 rows containing missing values (`geom_segment()`).

ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2, velocity = "stochastic", velocity_plot_type = "stream")
#> Warning: The dot-dot notation (`..step..`) was deprecated in ggplot2 3.4.0.
#> ℹ Please use `after_stat(step)` instead.
#> ℹ The deprecated feature was likely used in the SCP package.
#>   Please report the issue to the authors.

ClassDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2,
  label = TRUE, label_insitu = TRUE,
  velocity = "stochastic", velocity_plot_type = "stream", velocity_arrow_color = "yellow",
  velocity_density = 2, velocity_smooth = 1, streamline_n = 20, streamline_color = "black",
  show_stat = FALSE, legend.position = "none", theme_use = "theme_blank"
)