3D-Dimensional reduction plot for cell classification visualization.

Plotting cell points on a reduced 3D space and coloring according to the groups of the cells.

Usage

CellDimPlot3D(
  srt,
  group.by,
  reduction = NULL,
  dims = c(1, 2, 3),
  axis_labs = NULL,
  palette = "Paired",
  palcolor = NULL,
  bg_color = "grey80",
  pt.size = 1.5,
  cells.highlight = NULL,
  cols.highlight = "black",
  shape.highlight = "circle-open",
  sizes.highlight = 2,
  lineages = NULL,
  lineages_palette = "Dark2",
  span = 0.75,
  width = NULL,
  height = NULL,
  save = NULL,
  force = FALSE
)

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 three-length numeric vector specifying x-, y- and z-dimensions
axis_labs: A character vector of length 3 indicating the labels for the axes.
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.
pt.size: Point size.
cells.highlight: A vector of cell names to highlight.
cols.highlight: Color used to highlight the cells.
shape.highlight: Shape of the cell to highlight. See scattergl-marker-symbol
sizes.highlight: Size of highlighted cell points.
lineages: Lineages/pseudotime to add to the plot. If specified, curves will be fitted using stats::loess method.
lineages_palette: Color palette used for lineages.
span: The span of the loess smoother for lineages line.
width: Width in pixels, defaults to automatic sizing.
height: Height in pixels, defaults to automatic sizing.
save: The name of the file to save the plot to. Must end in ".html".
force: Whether to force drawing regardless of maximum levels in any cell group is greater than 100.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 13:07:22] Start standard scop workflow...
#> ℹ [2025-09-20 13:07:23] Checking a list of <Seurat> object...
#> ! [2025-09-20 13:07:23] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:07:23] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:07:25] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:07:25] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:07:26] Number of available HVF: 2000
#> ℹ [2025-09-20 13:07:26] Finished check
#> ℹ [2025-09-20 13:07:26] Perform `Seurat::ScaleData()`
#> Warning: Different features in new layer data than already exists for scale.data
#> ℹ [2025-09-20 13:07:26] 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 13:07:27] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 13:07:27] Reorder clusters...
#> ! [2025-09-20 13:07:27] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 13:07:27] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 13:07:27] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:07:27] UMAP will return its model
#> ℹ [2025-09-20 13:07:30] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:07:30] UMAP will return its model
#> ✔ [2025-09-20 13:07:34] Run scop standard workflow done
CellDimPlot3D(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "StandardpcaUMAP3D"
)


pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "StandardpcaUMAP3D",
  show_plot = FALSE
)
CellDimPlot3D(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "StandardpcaUMAP3D",
  lineages = "Lineage1"
)

3D-Dimensional reduction plot for cell classification visualization.

Usage

Arguments

See also

Examples