Visualize cell groups on a 2-dimensional reduction plot

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

Usage

CellDimPlot(
  srt,
  group.by,
  reduction = NULL,
  dims = c(1, 2),
  split.by = NULL,
  cells = NULL,
  show_na = FALSE,
  show_stat = ifelse(identical(theme_use, "theme_blank"), FALSE, 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_palcolor = NULL,
  add_mark = FALSE,
  mark_type = c("hull", "ellipse", "rect", "circle"),
  mark_expand = unit(3, "mm"),
  mark_alpha = 0.1,
  mark_linetype = 1,
  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,
  stat.by = NULL,
  stat_type = "percent",
  stat_plot_type = "pie",
  stat_plot_position = c("stack", "dodge"),
  stat_plot_size = 0.15,
  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@assays[[1]])/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,
  streamline_L = 5,
  streamline_minL = 1,
  streamline_res = 1,
  streamline_n = 15,
  streamline_width = 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),
  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.
group.by: Name of one or more meta.data columns to group (color) cells by (for example, orig.ident).
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 column to split plot by.
cells: Subset cells to plot.
show_na: Whether to assign a color from the color palette to NA group. If FALSE, cell points with NA level will colored by bg_color.
show_stat: Whether to show statistical information on the plot.
pt.size: Point size.
pt.alpha: Point transparency.
palette: Name of a color palette name collected in SCP. Default is "Paired".
palcolor: Custom colors used to create a color palette.
bg_color: Color value for background(NA) points.
label: Whether to label the cell groups.
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 to place the raw labels (group names) in the center of the cells with the corresponding group. Default is FALSE, which using numbers instead of raw labels.
label_repel: Logical value indicating whether the label is repel away from the center points.
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.
cells.highlight: A vector of cell names to highlight.
cols.highlight: Color used to highlight the cells.
sizes.highlight: Size of highlighted cell points.
alpha.highlight: Transparency of highlighted cell points.
stroke.highlight: Border width of highlighted cell points.
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.
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.
stat.by: The name of a metadata column to stat.
stat_type: Set stat types ("percent" or "count").
stat_plot_type: Set the statistical plot type.
stat_plot_position: Position adjustment in statistical plot.
stat_plot_size: Set the statistical plot size. Defaults to 0.1
stat_plot_palette: Color palette used in statistical plot.
stat_palcolor: Custom colors used in statistical plot
stat_plot_alpha: Transparency of the statistical plot.
stat_plot_label: Whether to add labels in the statistical plot.
stat_plot_label_size: Label size in the statistical plot.
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.
paga: Specify the calculated paga results to add a PAGA graph layer to the plot.
paga_type: PAGA plot type. "connectivities" or "connectivities_tree".
paga_node_size: Size of the nodes in PAGA plot.
paga_edge_threshold: Threshold of edge connectivities in PAGA plot.
paga_edge_size: Size of edges in PAGA plot.
paga_edge_color: Color of edges in PAGA plot.
paga_edge_alpha: Transparency of edges in PAGA plot.
paga_transition_threshold: Threshold of transition edges in PAGA plot.
paga_transition_size: Size of transition edges in PAGA plot.
paga_transition_color: Color of transition edges in PAGA plot.
paga_transition_alpha: Transparency of transition edges in PAGA plot.
paga_show_transition: Whether to show transitions between edges.
velocity: Specify the calculated RNA velocity mode to add a velocity layer to the plot.
velocity_plot_type: Set the velocity plot type.
velocity_n_neighbors: Set the number of neighbors used in velocity plot.
velocity_density: Set the density value used in velocity plot.
velocity_smooth: Set the smooth value used in velocity plot.
velocity_scale: Set the scale value used in velocity plot.
velocity_min_mass: Set the min_mass value used in velocity plot.
velocity_cutoff_perc: Set the cutoff_perc value used in velocity plot.
velocity_arrow_color: Color of arrows in velocity plot.
velocity_arrow_angle: Angle of arrows in velocity plot.
streamline_L: Typical length of a streamline in x and y units
streamline_minL: Minimum length of segments to show.
streamline_res: Resolution parameter (higher numbers increases the resolution).
streamline_n: Number of points to draw.
streamline_width: Size of streamline.
streamline_alpha: Transparency of streamline.
streamline_color: Color of streamline.
streamline_palette: Color palette used for streamline.
streamline_palcolor: Custom colors used for streamline.
streamline_bg_color: Background color of streamline.
streamline_bg_stroke: Border width of streamline background.
hex: Whether to chane the plot type from point to the hexagonal bin.
hex.linewidth: Border width of hexagonal bins.
hex.count: Whether show cell counts in each hexagonal bin.
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 maximum levels in any cell group is greater than 100.
seed: Random seed set for reproducibility

Examples

data("pancreas_sub")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP")

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", theme_use = "theme_blank")

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", theme_use = ggplot2::theme_classic, theme_args = list(base_size = 16))

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP") %>% panel_fix(height = 2, raster = TRUE, dpi = 30)


# Highlight cells
CellDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP",
  cells.highlight = colnames(pancreas_sub)[pancreas_sub$SubCellType == "Epsilon"]
)

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


# Add group labels
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", label = TRUE)

CellDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP",
  label = TRUE, label.fg = "orange", label.bg = "red", label.size = 5
)

CellDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP",
  label = TRUE, label_insitu = TRUE
)

CellDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP",
  label = TRUE, label_insitu = TRUE, label_repel = TRUE, label_segment_color = "red"
)


# Add various shape of marks
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE)

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE, mark_expand = unit(1, "mm"))

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE, mark_alpha = 0.3)

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE, mark_linetype = 2)

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE, mark_type = "ellipse")

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE, mark_type = "rect")

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_mark = TRUE, mark_type = "circle")


# Add a density layer
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_density = TRUE)

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", add_density = TRUE, density_filled = TRUE)
#> Warning: Removed 396 rows containing missing values (`geom_raster()`).

CellDimPlot(pancreas_sub,
  group.by = "SubCellType", reduction = "UMAP",
  add_density = TRUE, density_filled = TRUE, density_filled_palette = "Blues",
  cells.highlight = TRUE
)
#> Warning: Removed 396 rows containing missing values (`geom_raster()`).


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

CellDimPlot(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()`).

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


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

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

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


# Show neighbors graphs on the plot
pancreas_sub <- Standard_SCP(pancreas_sub)
#> [2023-11-21 07:06:39.451723] Start Standard_SCP
#> [2023-11-21 07:06:39.451863] 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 07:06:40.057438] Finished checking.
#> [2023-11-21 07:06:40.057585] Perform ScaleData on the data...
#> [2023-11-21 07:06:40.12836] 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 07:06:40.720841] Perform FindClusters (louvain) on the data...
#> [2023-11-21 07:06:40.792298] Reorder clusters...
#> [2023-11-21 07:06:40.854979] 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 07:06:47.487638] Standard_SCP done
#> Elapsed time: 8.04 secs 
CellDimPlot(pancreas_sub, group.by = "CellType", reduction = "UMAP", graph = "Standardpca_SNN")

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


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

CellDimPlot(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()`).

CellDimPlot(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()`).

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


# Show PAGA results 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' 'pytables' '--quiet' '-c' 'conda-forge'
#> Try to install leidenalg==0.10.1,matplotlib==3.6.3,numba==0.55.2,numpy==1.21.6,palantir==1.0.1,pandas==1.3.5,python-igraph==0.10.2,scanpy==1.9.5,scikit-learn==1.3.2,scipy==1.10.1,scvelo==0.2.5,wot==1.0.8.post2,trimap==1.1.4,pacmap==0.7.0,phate==1.0.11,bbknn==1.6.0,scanorama==1.7.4,scvi-tools==0.20.3 ...
#> ====================== 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:        3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36)  [GCC 12.3.0]
#> 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.
#> Error in slot(object = object, name = "features")[[layer]] <- features: more elements supplied than there are to replace
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", paga = pancreas_sub@misc$paga)

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

CellDimPlot(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,
  legend.position = "none", theme_use = "theme_blank"
)


# Show RNA velocity results 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.
#> Error in slot(object = object, name = "features")[[layer]] <- features: more elements supplied than there are to replace
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", paga = pancreas_sub@misc$paga, paga_show_transition = TRUE)

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = NA, velocity = "stochastic")
#> Error in VelocityPlot(srt, cells = cells, reduction = reduction, dims = dims,     velocity = velocity, plot_type = velocity_plot_type, group_by = group.by,     group_palette = palette, group_palcolor = palcolor, n_neighbors = velocity_n_neighbors,     density = velocity_density, smooth = velocity_smooth, scale = velocity_scale,     min_mass = velocity_min_mass, cutoff_perc = velocity_cutoff_perc,     arrow_color = velocity_arrow_color, arrow_angle = velocity_arrow_angle,     streamline_L = streamline_L, streamline_minL = streamline_minL,     streamline_res = streamline_res, streamline_n = streamline_n,     streamline_width = streamline_width, streamline_alpha = streamline_alpha,     streamline_color = streamline_color, streamline_palette = streamline_palette,     streamline_palcolor = streamline_palcolor, streamline_bg_color = streamline_bg_color,     streamline_bg_stroke = streamline_bg_stroke, aspect.ratio = aspect.ratio,     title = title, subtitle = subtitle, xlab = xlab, ylab = ylab,     legend.position = "bottom", legend.direction = legend.direction,     theme_use = theme_void, theme_args = theme_args, return_layer = TRUE): Cannot find the velocity embedding stochastic_UMAP.
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2, velocity = "stochastic", velocity_plot_type = "grid")
#> Error in VelocityPlot(srt, cells = cells, reduction = reduction, dims = dims,     velocity = velocity, plot_type = velocity_plot_type, group_by = group.by,     group_palette = palette, group_palcolor = palcolor, n_neighbors = velocity_n_neighbors,     density = velocity_density, smooth = velocity_smooth, scale = velocity_scale,     min_mass = velocity_min_mass, cutoff_perc = velocity_cutoff_perc,     arrow_color = velocity_arrow_color, arrow_angle = velocity_arrow_angle,     streamline_L = streamline_L, streamline_minL = streamline_minL,     streamline_res = streamline_res, streamline_n = streamline_n,     streamline_width = streamline_width, streamline_alpha = streamline_alpha,     streamline_color = streamline_color, streamline_palette = streamline_palette,     streamline_palcolor = streamline_palcolor, streamline_bg_color = streamline_bg_color,     streamline_bg_stroke = streamline_bg_stroke, aspect.ratio = aspect.ratio,     title = title, subtitle = subtitle, xlab = xlab, ylab = ylab,     legend.position = "bottom", legend.direction = legend.direction,     theme_use = theme_void, theme_args = theme_args, return_layer = TRUE): Cannot find the velocity embedding stochastic_UMAP.
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2, velocity = "stochastic", velocity_plot_type = "grid", velocity_scale = 1.5)
#> Error in VelocityPlot(srt, cells = cells, reduction = reduction, dims = dims,     velocity = velocity, plot_type = velocity_plot_type, group_by = group.by,     group_palette = palette, group_palcolor = palcolor, n_neighbors = velocity_n_neighbors,     density = velocity_density, smooth = velocity_smooth, scale = velocity_scale,     min_mass = velocity_min_mass, cutoff_perc = velocity_cutoff_perc,     arrow_color = velocity_arrow_color, arrow_angle = velocity_arrow_angle,     streamline_L = streamline_L, streamline_minL = streamline_minL,     streamline_res = streamline_res, streamline_n = streamline_n,     streamline_width = streamline_width, streamline_alpha = streamline_alpha,     streamline_color = streamline_color, streamline_palette = streamline_palette,     streamline_palcolor = streamline_palcolor, streamline_bg_color = streamline_bg_color,     streamline_bg_stroke = streamline_bg_stroke, aspect.ratio = aspect.ratio,     title = title, subtitle = subtitle, xlab = xlab, ylab = ylab,     legend.position = "bottom", legend.direction = legend.direction,     theme_use = theme_void, theme_args = theme_args, return_layer = TRUE): Cannot find the velocity embedding stochastic_UMAP.
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = 5, pt.alpha = 0.2, velocity = "stochastic", velocity_plot_type = "stream")
#> Error in VelocityPlot(srt, cells = cells, reduction = reduction, dims = dims,     velocity = velocity, plot_type = velocity_plot_type, group_by = group.by,     group_palette = palette, group_palcolor = palcolor, n_neighbors = velocity_n_neighbors,     density = velocity_density, smooth = velocity_smooth, scale = velocity_scale,     min_mass = velocity_min_mass, cutoff_perc = velocity_cutoff_perc,     arrow_color = velocity_arrow_color, arrow_angle = velocity_arrow_angle,     streamline_L = streamline_L, streamline_minL = streamline_minL,     streamline_res = streamline_res, streamline_n = streamline_n,     streamline_width = streamline_width, streamline_alpha = streamline_alpha,     streamline_color = streamline_color, streamline_palette = streamline_palette,     streamline_palcolor = streamline_palcolor, streamline_bg_color = streamline_bg_color,     streamline_bg_stroke = streamline_bg_stroke, aspect.ratio = aspect.ratio,     title = title, subtitle = subtitle, xlab = xlab, ylab = ylab,     legend.position = "bottom", legend.direction = legend.direction,     theme_use = theme_void, theme_args = theme_args, return_layer = TRUE): Cannot find the velocity embedding stochastic_UMAP.
CellDimPlot(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",
  legend.position = "none", theme_use = "theme_blank"
)
#> Error in VelocityPlot(srt, cells = cells, reduction = reduction, dims = dims,     velocity = velocity, plot_type = velocity_plot_type, group_by = group.by,     group_palette = palette, group_palcolor = palcolor, n_neighbors = velocity_n_neighbors,     density = velocity_density, smooth = velocity_smooth, scale = velocity_scale,     min_mass = velocity_min_mass, cutoff_perc = velocity_cutoff_perc,     arrow_color = velocity_arrow_color, arrow_angle = velocity_arrow_angle,     streamline_L = streamline_L, streamline_minL = streamline_minL,     streamline_res = streamline_res, streamline_n = streamline_n,     streamline_width = streamline_width, streamline_alpha = streamline_alpha,     streamline_color = streamline_color, streamline_palette = streamline_palette,     streamline_palcolor = streamline_palcolor, streamline_bg_color = streamline_bg_color,     streamline_bg_stroke = streamline_bg_stroke, aspect.ratio = aspect.ratio,     title = title, subtitle = subtitle, xlab = xlab, ylab = ylab,     legend.position = "bottom", legend.direction = legend.direction,     theme_use = theme_void, theme_args = theme_args, return_layer = TRUE): Cannot find the velocity embedding stochastic_UMAP.

Visualize cell groups on a 2-dimensional reduction plot

Usage

Arguments

See also

Examples