Cell Dimensional Plot

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 = grid::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 = grid::arrow(length = grid::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_scop",
  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 scop. 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.
add_mark: Whether to add marks around cell groups. Default is FALSE.
mark_type: Type of mark to add around cell groups. One of "hull", "ellipse", "rect", or "circle". Default is "hull".
mark_expand: Expansion of the mark around the cell group. Default is grid::unit(3, "mm").
mark_alpha: Transparency of the mark. Default is 0.1.
mark_linetype: Line type of the mark border. Default is 1 (solid line).
lineages: Lineages/pseudotime to add to the plot. If specified, curves will be fitted using stats::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 stats::loess method.
lineages_palette: Color palette used for lineages.
lineages_palcolor: Custom colors used for lineages.
lineages_arrow: Set arrows of the lineages. See grid::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)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 11:40:58] Start standard scop workflow...
#> ℹ [2025-11-13 11:40:59] Checking a list of <Seurat> object...
#> ! [2025-11-13 11:40:59] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 11:40:59] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:41:01] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:41:01] Use the separate HVF from srt_list
#> ℹ [2025-11-13 11:41:01] Number of available HVF: 2000
#> ℹ [2025-11-13 11:41:01] Finished check
#> ℹ [2025-11-13 11:41:02] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 11:41:02] Perform pca linear dimension reduction
#> StandardPC_ 1 
#> Positive:  Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2 
#> 	   Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1 
#> 	   Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5 
#> Negative:  Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1 
#> 	   Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19 
#> 	   Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1 
#> StandardPC_ 2 
#> Positive:  Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b 
#> 	   Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8 
#> 	   Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2 
#> Negative:  Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2 
#> 	   Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x 
#> 	   Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1 
#> StandardPC_ 3 
#> Positive:  Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1 
#> 	   2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr 
#> 	   Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1 
#> Negative:  Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1 
#> 	   Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1 
#> 	   Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a 
#> StandardPC_ 4 
#> Positive:  Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx 
#> 	   Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf 
#> 	   Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam 
#> Negative:  Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt 
#> 	   G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a 
#> 	   Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2 
#> StandardPC_ 5 
#> Positive:  Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a 
#> 	   Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7 
#> 	   Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694 
#> Negative:  Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2 
#> 	   Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b 
#> 	   Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3 
#> ℹ [2025-11-13 11:41:03] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 11:41:03] Reorder clusters...
#> ℹ [2025-11-13 11:41:03] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 11:41:03] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:41:03] UMAP will return its model
#> ℹ [2025-11-13 11:41:06] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:41:06] UMAP will return its model
#> ✔ [2025-11-13 11:41:09] Run scop standard workflow done
p1 <- CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP"
)
p1


thisplot::panel_fix(
  p1,
  height = 2,
  raster = TRUE,
  dpi = 30
)


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)
)


# 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 = grid::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
)


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_density = TRUE,
  density_filled = TRUE,
  density_filled_palette = "Blues",
  cells.highlight = TRUE
)


# 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
)


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
)
#> ◌ [2025-11-13 11:41:19] Installing: hexbin...
#>  
#> → Will install 1 package.
#> → The package (0 B) is cached.
#> + hexbin   1.28.5 
#>   
#> ℹ No downloads are needed, 1 pkg is cached
#> ✔ Installed hexbin 1.28.5  (1s)
#> ✔ 1 pkg + 1 dep: kept 1, added 1 [1.8s]
#> ✔ [2025-11-13 11:41:21] hexbin installed successfully


CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  hex = TRUE,
  hex.bins = 20
)
#> ✔ [2025-11-13 11:41:21] hexbin installed successfully


CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  hex = TRUE,
  hex.count = FALSE
)
#> ✔ [2025-11-13 11:41:22] hexbin installed successfully


# Show neighbors graphs on the plot
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 11:41:22] Start standard scop workflow...
#> ℹ [2025-11-13 11:41:22] Checking a list of <Seurat> object...
#> ℹ [2025-11-13 11:41:22] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 11:41:22] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:41:23] Use the separate HVF from srt_list
#> ℹ [2025-11-13 11:41:23] Number of available HVF: 2000
#> ℹ [2025-11-13 11:41:23] Finished check
#> ℹ [2025-11-13 11:41:23] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 11:41:23] Perform pca linear dimension reduction
#> StandardPC_ 1 
#> Positive:  Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2 
#> 	   Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1 
#> 	   Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5 
#> Negative:  Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1 
#> 	   Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19 
#> 	   Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1 
#> StandardPC_ 2 
#> Positive:  Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b 
#> 	   Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8 
#> 	   Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2 
#> Negative:  Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2 
#> 	   Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x 
#> 	   Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1 
#> StandardPC_ 3 
#> Positive:  Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1 
#> 	   2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr 
#> 	   Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1 
#> Negative:  Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1 
#> 	   Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1 
#> 	   Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a 
#> StandardPC_ 4 
#> Positive:  Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx 
#> 	   Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf 
#> 	   Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam 
#> Negative:  Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt 
#> 	   G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a 
#> 	   Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2 
#> StandardPC_ 5 
#> Positive:  Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a 
#> 	   Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7 
#> 	   Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694 
#> Negative:  Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2 
#> 	   Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b 
#> 	   Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3 
#> ℹ [2025-11-13 11:41:24] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 11:41:24] Reorder clusters...
#> ℹ [2025-11-13 11:41:24] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 11:41:24] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:41:24] UMAP will return its model
#> ℹ [2025-11-13 11:41:28] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:41:28] UMAP will return its model
#> ✔ [2025-11-13 11:41:31] Run scop standard workflow done
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 based on the pseudotime
pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  show_plot = FALSE
)

FeatureDimPlot(
  pancreas_sub,
  features = paste0("Lineage", 1:2),
  reduction = "UMAP"
)


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2)
)


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2),
  lineages_whiskers = TRUE
)


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2),
  lineages_span = 0.1
)


if (FALSE) { # \dontrun{
# Show PAGA results on the plot
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunPAGA(
  pancreas_sub,
  group_by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  return_seurat = TRUE
)

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
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunSCVELO(
  pancreas_sub,
  group_by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  mode = "stochastic",
  return_seurat = TRUE
)

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"
)

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  pt.size = 5,
  pt.alpha = 0.2,
  velocity = "stochastic",
  velocity_plot_type = "grid"
)

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
)

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  pt.size = 5,
  pt.alpha = 0.2,
  velocity = "stochastic",
  velocity_plot_type = "stream"
)

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"
)
} # }

Usage

Arguments

See also

Examples