This function creates a velocity plot for a given Seurat object. The plot shows the velocity vectors of the cells in a specified reduction space.
Usage
VelocityPlot(
srt,
reduction,
dims = c(1, 2),
cells = NULL,
velocity = "stochastic",
plot_type = c("raw", "grid", "stream"),
group_by = NULL,
group_palette = "Paired",
group_palcolor = NULL,
n_neighbors = ceiling(ncol(srt@assays[[1]])/50),
density = 1,
smooth = 0.5,
scale = 1,
min_mass = 1,
cutoff_perc = 5,
arrow_angle = 20,
arrow_color = "black",
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,
aspect.ratio = 1,
title = "Cell velocity",
subtitle = NULL,
xlab = NULL,
ylab = NULL,
legend.position = "right",
legend.direction = "vertical",
theme_use = "theme_scop",
theme_args = list(),
return_layer = FALSE,
seed = 11
)Arguments
- srt
A Seurat object.
- reduction
Name of the reduction in the Seurat object to use for plotting.
- dims
Indices of the dimensions to use for plotting.
- cells
Cells to include in the plot. If `NULL`, all cells will be included.
- velocity
Name of the velocity to use for plotting. Default is `"stochastic"`.
- plot_type
Type of plot to create. Can be `"raw"`, `"grid"`, or `"stream"`.
- group_by
Name of the column in the Seurat object metadata to group the cells by. Defaults is `NULL`.
- group_palette
Name of the palette to use for coloring the groups. Defaults is `"Paired"`.
- group_palcolor
Colors to use for coloring the groups. Defaults is `NULL`.
- n_neighbors
Number of neighbors to include for the density estimation. Defaults is `ceiling(ncol(srt@assays[[1]]) / 50)`.
- density
Propotion of cells to plot. Defaults is `1` (plot all cells).
- smooth
Smoothing parameter for density estimation. Defaults is `0.5`.
- scale
Scaling factor for the velocity vectors. Defaults is `1`.
- min_mass
Minimum mass value for the density-based cutoff. Defaults is `1`.
- cutoff_perc
Percentile value for the density-based cutoff. Defaults is `5`.
- arrow_angle
Angle of the arrowheads. Defaults is `20`.
- arrow_color
Color of the arrowheads. Defaults is `"black"`.
- streamline_L
Length of the streamlines. Defaults is `5`.
- streamline_minL
Minimum length of the streamlines. Defaults is `1`.
- streamline_res
Resolution of the streamlines. Defaults is `1`.
- streamline_n
Number of streamlines to plot. Defaults is `15`.
- streamline_width
Width of the streamlines. Defaults is `c(0, 0.8)`.
- streamline_alpha
Alpha transparency of the streamlines. Defaults is `1`.
- streamline_color
Color of the streamlines. Defaults is `NULL`.
- streamline_palette
Name of the palette to use for coloring the streamlines. Defaults is `"RdYlBu"`.
- streamline_palcolor
Colors to use for coloring the streamlines. Defaults is `NULL`.
- streamline_bg_color
Background color of the streamlines. Defaults is `"white"`.
- streamline_bg_stroke
Stroke width of the streamlines background. Defaults is `0.5`.
- aspect.ratio
Aspect ratio of the plot. Defaults is 1.
- title
Title of the plot. Defaults is `"Cell velocity"`.
- subtitle
Subtitle of the plot. Defaults is NULL.
- xlab
x-axis label. Defaults is NULL.
- ylab
y-axis label. Defaults is NULL.
- legend.position
Position of the legend. Defaults is `"right"`.
- legend.direction
Direction of the legend. Defaults is `"vertical"`.
- theme_use
Name of the theme to use for plotting. Defaults is `"theme_scop"`.
- theme_args
List of theme arguments for customization. Defaults is `list()`.
- return_layer
Whether to return the plot layers as a list. Defaults is `FALSE`.
- seed
Random seed for reproducibility. Defaults is `11`.
Examples
PrepareEnv()
#> ℹ [2025-09-20 14:00:06] Preparing scop Python Environment
#> ℹ [2025-09-20 14:00:06] Environment name: scop_env
#> ℹ [2025-09-20 14:00:06] Python version: 3.10-1
#> ℹ [2025-09-20 14:00:06] Number of packages to install: 21
#> ℹ [2025-09-20 14:00:07] Auto-detecting conda...
#> ℹ [2025-09-20 14:00:07] Using existing environment: /usr/share/miniconda/envs/scop_env
#> ℹ [2025-09-20 14:00:07] Checking and installing required packages...
#> ℹ [2025-09-20 14:00:07] Installing conda packages
#> ℹ [2025-09-20 14:00:09] Checking 3 packages in environment: scop_env
#> ℹ [2025-09-20 14:00:09] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:00:11] Found 197 packages installed
#> ✔ [2025-09-20 14:00:11] leidenalg 0.10.2
#> ✔ [2025-09-20 14:00:11] tbb 2022.2.0
#> ✔ [2025-09-20 14:00:11] python-igraph 0.11.9
#> ℹ [2025-09-20 14:00:11] Installing pip packages
#> ℹ [2025-09-20 14:00:13] Checking 18 packages in environment: scop_env
#> ℹ [2025-09-20 14:00:14] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:00:16] Found 197 packages installed
#> ✔ [2025-09-20 14:00:16] matplotlib 3.10.3
#> ✔ [2025-09-20 14:00:16] numba 0.59.1
#> ✔ [2025-09-20 14:00:16] llvmlite 0.42.0
#> ✔ [2025-09-20 14:00:16] numpy 1.26.4
#> ✔ [2025-09-20 14:00:16] palantir 1.4.1
#> ✔ [2025-09-20 14:00:16] pandas 2.0.3
#> ✔ [2025-09-20 14:00:16] scanpy 1.11.3
#> ✔ [2025-09-20 14:00:16] scikit-learn 1.7.0
#> ✔ [2025-09-20 14:00:16] scipy 1.15.3
#> ✔ [2025-09-20 14:00:16] scvelo 0.3.3
#> ✔ [2025-09-20 14:00:16] wot 1.0.8.post2
#> ✔ [2025-09-20 14:00:16] trimap 1.1.4
#> ✔ [2025-09-20 14:00:16] pacmap 0.8.0
#> ✔ [2025-09-20 14:00:16] phate 1.0.11
#> ✔ [2025-09-20 14:00:16] bbknn 1.6.0
#> ✔ [2025-09-20 14:00:16] scanorama 1.7.4
#> ✔ [2025-09-20 14:00:16] scvi-tools 1.2.1
#> ✔ [2025-09-20 14:00:16] cellrank 2.0.7
#> ✔ [2025-09-20 14:00:18] 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
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 14:00:19] Start standard scop workflow...
#> ℹ [2025-09-20 14:00:20] Checking a list of <Seurat> object...
#> ! [2025-09-20 14:00:20] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 14:00:20] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 14:00:22] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 14:00:22] Use the separate HVF from srt_list
#> ℹ [2025-09-20 14:00:22] Number of available HVF: 2000
#> ℹ [2025-09-20 14:00:23] Finished check
#> ℹ [2025-09-20 14:00:23] Perform `Seurat::ScaleData()`
#> Warning: Different features in new layer data than already exists for scale.data
#> ℹ [2025-09-20 14:00:23] 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 [2025-09-20 14:00:24] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:00:24] Reorder clusters...
#> ! [2025-09-20 14:00:24] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:00:24] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:00:24] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:00:24] UMAP will return its model
#> ℹ [2025-09-20 14:00:29] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:00:29] UMAP will return its model
#> ✔ [2025-09-20 14:00:33] Run scop standard workflow done
pancreas_sub <- RunSCVELO(
pancreas_sub,
group_by = "SubCellType",
linear_reduction = "pca",
nonlinear_reduction = "umap",
return_seurat = TRUE
)
#> ℹ [2025-09-20 14:00:35] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:00:35] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:00:37] Found 197 packages installed
#> ✔ [2025-09-20 14:00:37] scvelo version: 0.3.3
#> ℹ [2025-09-20 14:00:39] Checking 2 packages in environment: scop_env
#> ℹ [2025-09-20 14:00:40] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:00:42] Found 197 packages installed
#> ✔ [2025-09-20 14:00:42] scanpy version: 1.11.3
#> ✔ [2025-09-20 14:00:42] numpy version: 1.26.4
#> ℹ [2025-09-20 14:00:42] Converting <Seurat> to <AnnData> ...
#> ! [2025-09-20 14:00:42] "misc" slot is not converted
#> ! [2025-09-20 14:00:42] "tools" slot is not converted
#> ✔ [2025-09-20 14:00:42] Convert <Seurat> object to <AnnData> object completed
#> ℹ [2025-09-20 14:00:42] Running scVelo analysis...
#> ✔ [2025-09-20 14:00:47] scVelo analysis completed
#> ℹ [2025-09-20 14:00:47] Converting <AnnData> object to <Seurat> object...
#> ✔ [2025-09-20 14:00:51] 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
VelocityPlot(
pancreas_sub,
reduction = "UMAP"
)
#> ℹ [2025-09-20 14:00:55] metR installed successfully
VelocityPlot(
pancreas_sub,
reduction = "UMAP",
group_by = "SubCellType"
)
#> ℹ [2025-09-20 14:00:55] metR installed successfully
#> Error in vapply(glyph, get_attr, which = "width", default = 0, numeric(1)): values must be length 1,
#> but FUN(X[[1]]) result is length 1000
VelocityPlot(
pancreas_sub,
reduction = "UMAP",
plot_type = "grid"
)
#> ℹ [2025-09-20 14:00:55] metR installed successfully
VelocityPlot(
pancreas_sub,
reduction = "UMAP",
plot_type = "stream"
)
#> ℹ [2025-09-20 14:00:56] metR installed successfully
#> ℹ [2025-09-20 14:00:56] metR installed successfully
VelocityPlot(
pancreas_sub,
reduction = "UMAP",
plot_type = "stream",
streamline_color = "black"
)
#> ℹ [2025-09-20 14:00:56] metR installed successfully
#> ℹ [2025-09-20 14:00:57] metR installed successfully
VelocityPlot(
pancreas_sub,
reduction = "UMAP",
plot_type = "stream",
streamline_color = "black",
arrow_color = "red"
)
#> ℹ [2025-09-20 14:00:57] metR installed successfully
#> ℹ [2025-09-20 14:00:57] metR installed successfully
