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Visualize cell groups on a 2-dimensional reduction plot

Source: R/CellDimPlot.R
CellDimPlot.Rd

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

See also

FeatureDimPlot

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 13:01:44] Start standard scop workflow...
#> ℹ [2025-09-20 13:01:45] Checking a list of <Seurat> object...
#> ! [2025-09-20 13:01:45] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:01:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:01:47] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:01:47] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:01:47] Number of available HVF: 2000
#> ℹ [2025-09-20 13:01:47] Finished check
#> ℹ [2025-09-20 13:01:48] Perform `Seurat::ScaleData()`
#> Warning: Different features in new layer data than already exists for scale.data
#> ℹ [2025-09-20 13:01:48] 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 [2025-09-20 13:01:49] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 13:01:49] Reorder clusters...
#> ! [2025-09-20 13:01:49] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 13:01:49] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 13:01:49] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:01:49] UMAP will return its model
#> ℹ [2025-09-20 13:01:52] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:01:52] UMAP will return its model
#> ✔ [2025-09-20 13:01:55] Run scop standard workflow done
p1 <- CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP"
)
p1
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


panel_fix(
  p1,
  height = 2,
  raster = TRUE,
  dpi = 30
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> ℹ [2025-09-20 13:01:55] Installing: ragg...
#>  
#> → Will install 5 packages.
#> → All 5 packages (0 B) are cached.
#> + base64enc     0.1-3 
#> + ragg          1.5.0  + ✔ libfreetype6-dev, ✔ libjpeg-dev, ✔ libpng-dev, ✔ libtiff-dev, ✔ libwebp-dev
#> + stringi       1.8.7  + ✔ libicu-dev
#> + systemfonts   1.2.3  + ✔ libfontconfig1-dev, ✔ libfreetype6-dev
#> + textshaping   1.0.3  + ✔ libfreetype6-dev, ✔ libfribidi-dev, ✔ libharfbuzz-dev
#> ✔ All system requirements are already installed.
#>   
#> ℹ No downloads are needed, 5 pkgs are cached
#> ✔ Got base64enc 0.1-3 (x86_64-pc-linux-gnu-ubuntu-24.04) (26.57 kB)
#> ✔ Got textshaping 1.0.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (188.95 kB)
#> ✔ Got systemfonts 1.2.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (350.85 kB)
#> ✔ Got ragg 1.5.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (743.63 kB)
#> ✔ Got stringi 1.8.7 (x86_64-pc-linux-gnu-ubuntu-24.04) (3.29 MB)
#> ℹ Installing system requirements
#> ℹ Executing `sudo sh -c apt-get -y update`
#> Get:1 file:/etc/apt/apt-mirrors.txt Mirrorlist [144 B]
#> Hit:2 http://azure.archive.ubuntu.com/ubuntu noble InRelease
#> Hit:3 http://azure.archive.ubuntu.com/ubuntu noble-updates InRelease
#> Hit:4 http://azure.archive.ubuntu.com/ubuntu noble-backports InRelease
#> Hit:5 http://azure.archive.ubuntu.com/ubuntu noble-security InRelease
#> Hit:6 https://packages.microsoft.com/repos/azure-cli noble InRelease
#> Hit:7 https://packages.microsoft.com/ubuntu/24.04/prod noble InRelease
#> Reading package lists...
#> ℹ Executing `sudo sh -c apt-get -y install libfreetype6-dev libjpeg-dev libpng-dev libtiff-dev libwebp-dev libicu-dev libfontconfig1-dev libfribidi-dev libharfbuzz-dev`
#> Reading package lists...
#> Building dependency tree...
#> Reading state information...
#> libfreetype-dev is already the newest version (2.13.2+dfsg-1build3).
#> libjpeg-dev is already the newest version (8c-2ubuntu11).
#> libpng-dev is already the newest version (1.6.43-5build1).
#> libtiff-dev is already the newest version (4.5.1+git230720-4ubuntu2.3).
#> libwebp-dev is already the newest version (1.3.2-0.4build3).
#> libicu-dev is already the newest version (74.2-1ubuntu3.1).
#> libfontconfig1-dev is already the newest version (2.15.0-1.1ubuntu2).
#> libfribidi-dev is already the newest version (1.0.13-3build1).
#> libharfbuzz-dev is already the newest version (8.3.0-2build2).
#> 0 upgraded, 0 newly installed, 0 to remove and 41 not upgraded.
#> ✔ Installed base64enc 0.1-3  (93ms)
#> ✔ Installed ragg 1.5.0  (120ms)
#> ✔ Installed stringi 1.8.7  (156ms)
#> ✔ Installed systemfonts 1.2.3  (169ms)
#> ✔ Installed textshaping 1.0.3  (74ms)
#> ✔ 1 pkg + 9 deps: kept 5, added 5, dld 5 (4.60 MB) [3.5s]
#> ℹ [2025-09-20 13:01:59] png and ragg installed successfully


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  theme_use = "theme_blank"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  theme_use = ggplot2::theme_classic,
  theme_args = list(base_size = 16)
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Highlight cells
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  cells.highlight = colnames(
    pancreas_sub
  )[pancreas_sub$SubCellType == "Epsilon"]
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  split.by = "Phase",
  reduction = "UMAP",
  cells.highlight = TRUE,
  theme_use = "theme_blank",
  legend.position = "none"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Add group labels
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  label = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  label = TRUE,
  label.fg = "orange",
  label.bg = "red",
  label.size = 5
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  label = TRUE,
  label_insitu = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  label = TRUE,
  label_insitu = TRUE,
  label_repel = TRUE,
  label_segment_color = "red"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Add various shape of marks
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE,
  mark_expand = grid::unit(1, "mm")
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE,
  mark_alpha = 0.3
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE,
  mark_linetype = 2
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE,
  mark_type = "ellipse"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE,
  mark_type = "rect"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_mark = TRUE,
  mark_type = "circle"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Add a density layer
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_density = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_density = TRUE,
  density_filled = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 396 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  add_density = TRUE,
  density_filled = TRUE,
  density_filled_palette = "Blues",
  cells.highlight = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 396 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# Add statistical charts
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  stat.by = "Phase"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


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: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  stat.by = "Phase",
  stat_plot_type = "bar",
  stat_type = "count",
  stat_plot_position = "dodge"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Chane the plot type from point to the hexagonal bin
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  hex = TRUE
)
#> ℹ [2025-09-20 13:02:08] 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
#> ✔ Got hexbin 1.28.5 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.60 MB)
#> ✔ Installed hexbin 1.28.5  (1s)
#> ✔ 1 pkg + 1 dep: kept 1, added 1, dld 1 (1.60 MB) [1.9s]
#> ℹ [2025-09-20 13:02:10] hexbin installed successfully
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_hex()`).


CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  hex = TRUE,
  hex.bins = 20
)
#> ℹ [2025-09-20 13:02:11] hexbin installed successfully
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_hex()`).


CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  hex = TRUE,
  hex.count = FALSE
)
#> ℹ [2025-09-20 13:02:11] hexbin installed successfully
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_hex()`).


# Show neighbors graphs on the plot
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 13:02:11] Start standard scop workflow...
#> ℹ [2025-09-20 13:02:11] Checking a list of <Seurat> object...
#> ℹ [2025-09-20 13:02:12] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2025-09-20 13:02:12] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:02:12] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:02:12] Number of available HVF: 2000
#> ℹ [2025-09-20 13:02:12] Finished check
#> ℹ [2025-09-20 13:02:12] Perform `Seurat::ScaleData()`
#> ℹ [2025-09-20 13:02:13] 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 [2025-09-20 13:02:14] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 13:02:14] Reorder clusters...
#> ! [2025-09-20 13:02:14] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 13:02:14] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 13:02:14] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:02:14] UMAP will return its model
#> Warning: Key ‘StandardpcaUMAP2D_’ taken, using ‘standardpcaumap2d_’ instead
#> ℹ [2025-09-20 13:02:17] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:02:17] UMAP will return its model
#> Warning: Key ‘StandardpcaUMAP3D_’ taken, using ‘standardpcaumap3d_’ instead
#> ✔ [2025-09-20 13:02:20] Run scop standard workflow done
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  graph = "Standardpca_SNN"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "UMAP",
  graph = "Standardpca_SNN",
  edge_color = "grey80"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# 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)
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_path()`).


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2),
  lineages_whiskers = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_path()`).


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2),
  lineages_span = 0.1
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Show PAGA results on the plot
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 13:02:25] Start standard scop workflow...
#> ℹ [2025-09-20 13:02:25] Checking a list of <Seurat> object...
#> ℹ [2025-09-20 13:02:26] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2025-09-20 13:02:26] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:02:26] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:02:26] Number of available HVF: 2000
#> ℹ [2025-09-20 13:02:26] Finished check
#> ℹ [2025-09-20 13:02:26] Perform `Seurat::ScaleData()`
#> ℹ [2025-09-20 13:02:27] 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 [2025-09-20 13:02:28] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 13:02:28] Reorder clusters...
#> ! [2025-09-20 13:02:28] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 13:02:28] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 13:02:28] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:02:28] UMAP will return its model
#> ℹ [2025-09-20 13:02:31] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:02:31] UMAP will return its model
#> ✔ [2025-09-20 13:02:34] Run scop standard workflow done
pancreas_sub <- RunPAGA(
  pancreas_sub,
  group_by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  return_seurat = TRUE
)
#> ! [2025-09-20 13:02:49] `envname`: "scop_env" python environment does not exist. Create it with `PrepareEnv()`
#> ℹ [2025-09-20 13:02:49] Preparing scop Python Environment
#> ℹ [2025-09-20 13:02:49] Environment name: scop_env
#> ℹ [2025-09-20 13:02:49] Python version: 3.10-1
#> ℹ [2025-09-20 13:02:49] Number of packages to install: 21
#> ℹ [2025-09-20 13:02:49] Auto-detecting conda...
#> ℹ [2025-09-20 13:02:50] Creating conda environment with Python "3.10-1"...
#> + /usr/share/miniconda/bin/conda create --yes --name scop_env 'python=3.10-1' pip setuptools wheel --quiet -c conda-forge
#> ✔ [2025-09-20 13:03:16] Environment created successfully: /usr/share/miniconda/envs/scop_env
#> ℹ [2025-09-20 13:03:16] Checking and installing required packages...
#> ℹ [2025-09-20 13:03:16] Installing conda packages
#> ℹ [2025-09-20 13:03:16] Try to install: leidenalg==0.10.2, tbb==2022.2.0, and python-igraph==0.11.9
#> ℹ [2025-09-20 13:03:16] Installing 3 packages into environment: scop_env
#> ℹ [2025-09-20 13:03:18] Installing packages via conda...
#> ℹ [2025-09-20 13:03:18] Using channel: "conda-forge"
#> ℹ [2025-09-20 13:03:18] Installing 3 packages...
#> + /usr/share/miniconda/bin/conda 'install' '--yes' '--name' 'scop_env' '-c' 'conda-forge' 'leidenalg==0.10.2' 'tbb==2022.2.0' 'python-igraph==0.11.9'
#> ✔ [2025-09-20 13:03:26] conda installation completed successfully
#> ℹ [2025-09-20 13:03:26] Checking 3 packages in environment: scop_env
#> ℹ [2025-09-20 13:03:27] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 13:03:28] Found 51 packages installed
#> ✔ [2025-09-20 13:03:28] leidenalg 0.10.2
#> ✔ [2025-09-20 13:03:28] tbb 2022.2.0
#> ✔ [2025-09-20 13:03:28] python-igraph 0.11.9
#> ℹ [2025-09-20 13:03:28] Installing pip packages
#> ℹ [2025-09-20 13:03:28] Try to install: matplotlib==3.10.3, numba==0.59.1, llvmlite==0.42.0, numpy==1.26.4, palantir==1.4.1, pandas==2.0.3, scanpy==1.11.3, scikit-learn==1.7.0, scipy==1.15.3, scvelo==0.3.3, wot==1.0.8.post2, trimap==1.1.4, pacmap==0.8.0, phate==1.0.11, bbknn==1.6.0, scanorama==1.7.4, scvi-tools==1.2.1, and cellrank==2.0.7
#> ℹ [2025-09-20 13:03:28] Installing 19 packages into environment: scop_env
#> ℹ [2025-09-20 13:03:30] Installing packages via pip...
#> ✔ [2025-09-20 13:05:52] pip installation completed
#> ℹ [2025-09-20 13:05:53] Checking 18 packages in environment: scop_env
#> ℹ [2025-09-20 13:05:54] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 13:05:56] Found 197 packages installed
#> ✔ [2025-09-20 13:05:56] matplotlib 3.10.3
#> ✔ [2025-09-20 13:05:56] numba 0.59.1
#> ✔ [2025-09-20 13:05:56] llvmlite 0.42.0
#> ✔ [2025-09-20 13:05:56] numpy 1.26.4
#> ✔ [2025-09-20 13:05:56] palantir 1.4.1
#> ✔ [2025-09-20 13:05:56] pandas 2.0.3
#> ✔ [2025-09-20 13:05:56] scanpy 1.11.3
#> ✔ [2025-09-20 13:05:56] scikit-learn 1.7.0
#> ✔ [2025-09-20 13:05:56] scipy 1.15.3
#> ✔ [2025-09-20 13:05:56] scvelo 0.3.3
#> ✔ [2025-09-20 13:05:56] wot 1.0.8.post2
#> ✔ [2025-09-20 13:05:56] trimap 1.1.4
#> ✔ [2025-09-20 13:05:56] pacmap 0.8.0
#> ✔ [2025-09-20 13:05:56] phate 1.0.11
#> ✔ [2025-09-20 13:05:56] bbknn 1.6.0
#> ✔ [2025-09-20 13:05:56] scanorama 1.7.4
#> ✔ [2025-09-20 13:05:56] scvi-tools 1.2.1
#> ✔ [2025-09-20 13:05:56] cellrank 2.0.7
#> ✔ [2025-09-20 13:06:01] Python Environment Ready
#> conda environment: 
#>   conda:          /usr/share/miniconda/bin/conda
#>   environment:    /usr/share/miniconda/envs/scop_env
#> python config: 
#>   python:         /usr/share/miniconda/envs/scop_env/bin/python3.10
#>   libpython:      /usr/share/miniconda/envs/scop_env/lib/libpython3.10.so
#>   pythonhome:     /usr/share/miniconda/envs/scop_env:/usr/share/miniconda/envs/scop_env
#>   version:        3.10.1 | packaged by conda-forge | (main, Dec 22 2021, 01:39:36) [GCC 9.4.0]
#>   numpy:          /usr/share/miniconda/envs/scop_env/lib/python3.10/site-packages/numpy
#>   numpy_version:  1.26.4
#>   
#>   NOTE: Python version was forced by use_python() function
#> ℹ [2025-09-20 13:06:02] Checking 2 packages in environment: scop_env
#> ℹ [2025-09-20 13:06:03] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 13:06:05] Found 197 packages installed
#> ✔ [2025-09-20 13:06:05] scanpy version: 1.11.3
#> ✔ [2025-09-20 13:06:05] numpy version: 1.26.4
#> ℹ [2025-09-20 13:06:05] Converting <Seurat> to <AnnData> ...
#> ! [2025-09-20 13:06:10] "misc" slot is not converted
#> ! [2025-09-20 13:06:10] "tools" slot is not converted
#> ✔ [2025-09-20 13:06:10] Convert <Seurat> object to <AnnData> object completed
#> ℹ [2025-09-20 13:06:10] Running PAGA analysis...
#> ✔ [2025-09-20 13:06:25] PAGA analysis completed
#> ℹ [2025-09-20 13:06:25] Converting <AnnData> object to <Seurat> object...
#> ✔ [2025-09-20 13:06:26] Convert <AnnData> object to <Seurat> object completed

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  paga = pancreas_sub@misc$paga
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  paga = pancreas_sub@misc$paga,
  paga_type = "connectivities_tree"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


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"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


# Show RNA velocity results on the plot
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 13:06:28] Start standard scop workflow...
#> ℹ [2025-09-20 13:06:28] Checking a list of <Seurat> object...
#> ℹ [2025-09-20 13:06:28] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2025-09-20 13:06:28] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:06:29] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:06:29] Number of available HVF: 2000
#> ℹ [2025-09-20 13:06:29] Finished check
#> ℹ [2025-09-20 13:06:29] Perform `Seurat::ScaleData()`
#> ℹ [2025-09-20 13:06:29] 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 [2025-09-20 13:06:30] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 13:06:30] Reorder clusters...
#> ! [2025-09-20 13:06:30] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 13:06:30] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 13:06:30] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:06:30] UMAP will return its model
#> Warning: Key ‘StandardpcaUMAP2D_’ taken, using ‘standardpcaumap2d_’ instead
#> ℹ [2025-09-20 13:06:34] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:06:34] UMAP will return its model
#> Warning: Key ‘StandardpcaUMAP3D_’ taken, using ‘standardpcaumap3d_’ instead
#> ✔ [2025-09-20 13:06:37] Run scop standard workflow done
pancreas_sub <- RunSCVELO(
  pancreas_sub,
  group_by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  mode = "stochastic",
  return_seurat = TRUE
)
#> ℹ [2025-09-20 13:06:38] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 13:06:39] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 13:06:41] Found 197 packages installed
#> ✔ [2025-09-20 13:06:41] scvelo version: 0.3.3
#> ℹ [2025-09-20 13:06:42] Checking 2 packages in environment: scop_env
#> ℹ [2025-09-20 13:06:43] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 13:06:45] Found 197 packages installed
#> ✔ [2025-09-20 13:06:45] scanpy version: 1.11.3
#> ✔ [2025-09-20 13:06:45] numpy version: 1.26.4
#> ℹ [2025-09-20 13:06:45] Converting <Seurat> to <AnnData> ...
#> ! [2025-09-20 13:06:45] "misc" slot is not converted
#> ! [2025-09-20 13:06:45] "tools" slot is not converted
#> ✔ [2025-09-20 13:06:45] Convert <Seurat> object to <AnnData> object completed
#> ℹ [2025-09-20 13:06:45] Running scVelo analysis...
#> ✔ [2025-09-20 13:06:51] scVelo analysis completed
#> ℹ [2025-09-20 13:06:51] Converting <AnnData> object to <Seurat> object...
#> ✔ [2025-09-20 13:06:56] Convert <AnnData> object to <Seurat> object completed
#> Warning: Different features in new layer data than already exists for counts
#> Warning: Different features in new layer data than already exists for data

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  paga = pancreas_sub@misc$paga,
  paga_show_transition = TRUE
)
#> Error in .subset2(x, i, exact = exact): attempt to select less than one element in get1index

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  pt.size = NA,
  velocity = "stochastic"
)
#> ℹ [2025-09-20 13:07:00] Installing: metR...
#>  
#> → Will install 20 packages.
#> → All 20 packages (0 B) are cached.
#> + Formula          1.2-5  
#> + backports        1.5.0  
#> + checkmate        2.3.3  
#> + classInt         0.4-11 
#> + data.table       1.17.8 
#> + e1071            1.7-16 
#> + formula.tools    1.7.1  
#> + lubridate        1.9.4  
#> + magrittr         2.0.4  
#> + metR             0.18.2 
#> + operator.tools   1.6.3  
#> + plyr             1.8.9  
#> + proxy            0.4-27 
#> + purrr            1.1.0  
#> + s2               1.1.9   + ✔ libabsl-dev, ✔ cmake, ✔ libssl-dev
#> + sf               1.0-21  + ✔ libgdal-dev, ✔ gdal-bin, ✔ libgeos-dev, ✔ libproj-dev, ✔ libsqlite3-dev
#> + stringr          1.5.2  
#> + timechange       0.3.0  
#> + units            0.8-7   + ✔ libudunits2-dev
#> + wk               0.9.4  
#> ✔ All system requirements are already installed.
#>   
#> ℹ No downloads are needed, 20 pkgs are cached
#> ✔ Got backports 1.5.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (115.90 kB)
#> ✔ Got Formula 1.2-5 (x86_64-pc-linux-gnu-ubuntu-24.04) (159.13 kB)
#> ✔ Got proxy 0.4-27 (x86_64-pc-linux-gnu-ubuntu-24.04) (175.47 kB)
#> ✔ Got formula.tools 1.7.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (87.50 kB)
#> ✔ Got classInt 0.4-11 (x86_64-pc-linux-gnu-ubuntu-24.04) (496.56 kB)
#> ✔ Got e1071 1.7-16 (x86_64-pc-linux-gnu-ubuntu-24.04) (596.57 kB)
#> ✔ Got operator.tools 1.6.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (52.84 kB)
#> ✔ Got stringr 1.5.2 (x86_64-pc-linux-gnu-ubuntu-24.04) (311.71 kB)
#> ✔ Got checkmate 2.3.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (777.39 kB)
#> ✔ Got purrr 1.1.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (554.62 kB)
#> ✔ Got timechange 0.3.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (174.23 kB)
#> ✔ Got units 0.8-7 (x86_64-pc-linux-gnu-ubuntu-24.04) (467.34 kB)
#> ✔ Got plyr 1.8.9 (x86_64-pc-linux-gnu-ubuntu-24.04) (787.35 kB)
#> ✔ Got wk 0.9.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.73 MB)
#> ✔ Got lubridate 1.9.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (990.67 kB)
#> ✔ Got data.table 1.17.8 (x86_64-pc-linux-gnu-ubuntu-24.04) (2.67 MB)
#> ✔ Got s2 1.1.9 (x86_64-pc-linux-gnu-ubuntu-24.04) (2.21 MB)
#> ✔ Got metR 0.18.2 (x86_64-pc-linux-gnu-ubuntu-24.04) (3.79 MB)
#> ✔ Got sf 1.0-21 (x86_64-pc-linux-gnu-ubuntu-24.04) (4.08 MB)
#> ℹ Installing system requirements
#> ℹ Executing `sudo sh -c apt-get -y update`
#> Get:1 file:/etc/apt/apt-mirrors.txt Mirrorlist [144 B]
#> Hit:2 http://azure.archive.ubuntu.com/ubuntu noble InRelease
#> Hit:6 https://packages.microsoft.com/repos/azure-cli noble InRelease
#> Hit:3 http://azure.archive.ubuntu.com/ubuntu noble-updates InRelease
#> Hit:7 https://packages.microsoft.com/ubuntu/24.04/prod noble InRelease
#> Hit:4 http://azure.archive.ubuntu.com/ubuntu noble-backports InRelease
#> Hit:5 http://azure.archive.ubuntu.com/ubuntu noble-security InRelease
#> Reading package lists...
#> ℹ Executing `sudo sh -c apt-get -y install libabsl-dev cmake libssl-dev libgdal-dev gdal-bin libgeos-dev libproj-dev libsqlite3-dev libudunits2-dev libicu-dev`
#> Reading package lists...
#> Building dependency tree...
#> Reading state information...
#> libabsl-dev is already the newest version (20220623.1-3.1ubuntu3.2).
#> cmake is already the newest version (3.28.3-1build7).
#> libssl-dev is already the newest version (3.0.13-0ubuntu3.5).
#> libgdal-dev is already the newest version (3.8.4+dfsg-3ubuntu3).
#> gdal-bin is already the newest version (3.8.4+dfsg-3ubuntu3).
#> libgeos-dev is already the newest version (3.12.1-3build1).
#> libproj-dev is already the newest version (9.4.0-1build2).
#> libsqlite3-dev is already the newest version (3.45.1-1ubuntu2.5).
#> libudunits2-dev is already the newest version (2.2.28-7build1).
#> libicu-dev is already the newest version (74.2-1ubuntu3.1).
#> 0 upgraded, 0 newly installed, 0 to remove and 41 not upgraded.
#> ✔ Installed backports 1.5.0  (63ms)
#> ✔ Installed checkmate 2.3.3  (86ms)
#> ✔ Installed classInt 0.4-11  (108ms)
#> ✔ Installed data.table 1.17.8  (148ms)
#> ✔ Installed e1071 1.7-16  (70ms)
#> ✔ Installed Formula 1.2-5  (59ms)
#> ✔ Installed formula.tools 1.7.1  (61ms)
#> ✔ Installed lubridate 1.9.4  (62ms)
#> ✔ Installed magrittr 2.0.4  (94ms)
#> ✔ Installed metR 0.18.2  (63ms)
#> ✔ Installed operator.tools 1.6.3  (63ms)
#> ✔ Installed plyr 1.8.9  (63ms)
#> ✔ Installed proxy 0.4-27  (62ms)
#> ✔ Installed purrr 1.1.0  (64ms)
#> ✔ Installed s2 1.1.9  (67ms)
#> ✔ Installed stringr 1.5.2  (1s)
#> ✔ Installed sf 1.0-21  (1.1s)
#> ✔ Installed timechange 0.3.0  (67ms)
#> ✔ Installed units 0.8-7  (63ms)
#> ✔ Installed wk 0.9.4  (46ms)
#> ✔ 1 pkg + 46 deps: kept 27, added 20, dld 19 (20.23 MB) [7.3s]
#> ℹ [2025-09-20 13:07:07] metR installed successfully
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 1000 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: Removed 1000 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_segment()`).


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  pt.size = 5,
  pt.alpha = 0.2,
  velocity = "stochastic",
  velocity_plot_type = "grid"
)
#> ℹ [2025-09-20 13:07:08] metR installed successfully
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Error in cbind_gtable(x, y, size = size): `x` and `y` must have the same number of rows

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
)
#> ℹ [2025-09-20 13:07:09] metR installed successfully
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Error in cbind_gtable(x, y, size = size): `x` and `y` must have the same number of rows

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  pt.size = 5,
  pt.alpha = 0.2,
  velocity = "stochastic",
  velocity_plot_type = "stream"
)
#> ℹ [2025-09-20 13:07:10] metR installed successfully
#> ℹ [2025-09-20 13:07:10] metR installed successfully
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the scop package.
#>   Please report the issue at <https://github.com/mengxu98/scop/issues>.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


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"
)
#> ℹ [2025-09-20 13:07:11] metR installed successfully
#> ℹ [2025-09-20 13:07:11] metR installed successfully
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Error in cbind_gtable(x, y, size = size): `x` and `y` must have the same number of rows