Plot dynamic features across pseudotime
Usage
DynamicPlot(
srt,
lineages,
features,
group.by = NULL,
cells = NULL,
layer = "counts",
assay = NULL,
family = NULL,
exp_method = c("log1p", "raw", "zscore", "fc", "log2fc"),
lib_normalize = identical(layer, "counts"),
libsize = NULL,
compare_lineages = TRUE,
compare_features = FALSE,
add_line = TRUE,
add_interval = TRUE,
line.size = 1,
line_palette = "Dark2",
line_palcolor = NULL,
add_point = TRUE,
pt.size = 1,
point_palette = "Paired",
point_palcolor = NULL,
add_rug = TRUE,
flip = FALSE,
reverse = FALSE,
x_order = c("value", "rank"),
aspect.ratio = NULL,
legend.position = "right",
legend.direction = "vertical",
theme_use = "theme_scop",
theme_args = list(),
combine = TRUE,
nrow = NULL,
ncol = NULL,
byrow = TRUE,
seed = 11
)Arguments
- srt
A Seurat object.
- lineages
A character vector specifying the lineages to plot.
- features
A character vector specifying the features to plot.
- group.by
A character specifying a metadata column to group the cells by. Default is NULL.
- cells
A character vector specifying the cells to include in the plot. Default is NULL.
- layer
A character string specifying the layer to use for the analysis. Default is "counts".
- assay
A character string specifying the assay to use for the analysis. Default is NULL.
- family
A character specifying the model used to calculate the dynamic features if needed. By default, this parameter is set to NULL, and the appropriate family will be automatically determined.
- exp_method
A character specifying the method to transform the expression values. Default is "log1p" with options "log1p", "raw", "zscore", "fc", "log2fc".
- lib_normalize
A boolean specifying whether to normalize the expression values using library size. Default the
layeris counts, this parameter is set to TRUE. Otherwise, it is set to FALSE.- libsize
A numeric vector specifying the library size for each cell. Default is NULL.
- compare_lineages
A boolean specifying whether to compare the lineages in the plot. Default is TRUE.
- compare_features
A boolean specifying whether to compare the features in the plot. Default is FALSE.
- add_line
A boolean specifying whether to add lines to the plot. Default is TRUE.
- add_interval
A boolean specifying whether to add confidence intervals to the plot. Default is TRUE.
- line.size
A numeric specifying the size of the lines. Default is 1.
- line_palette
A character string specifying the name of the palette to use for the line colors. Default is "Dark2".
- line_palcolor
A vector specifying the colors to use for the line palette. Default is NULL.
- add_point
A boolean specifying whether to add points to the plot. Default is TRUE.
- pt.size
A numeric specifying the size of the points. Default is 1.
- point_palette
A character string specifying the name of the palette to use for the point colors. Default is "Paired".
- point_palcolor
A vector specifying the colors to use for the point palette. Default is NULL.
- add_rug
A boolean specifying whether to add rugs to the plot. Default is TRUE.
- flip
A boolean specifying whether to flip the x-axis. Default is FALSE.
- reverse
A boolean specifying whether to reverse the x-axis. Default is FALSE.
- x_order
A character specifying the order of the x-axis values. Default is c("value", "rank").
- aspect.ratio
A numeric specifying the aspect ratio of the plot. Default is NULL.
- legend.position
A character string specifying the position of the legend in the plot. Default is "right".
- legend.direction
A character string specifying the direction of the legend in the plot. Default is "vertical".
- theme_use
A character string specifying the name of the theme to use for the plot. Default is "theme_scop".
- theme_args
A list specifying the arguments to pass to the theme function. Default is list().
- combine
A boolean specifying whether to combine multiple plots into a single plot. Default is TRUE.
- nrow
A numeric specifying the number of rows in the combined plot. Default is NULL.
- ncol
A numeric specifying the number of columns in the combined plot. Default is NULL.
- byrow
A boolean specifying whether to fill plots by row in the combined plot. Default is TRUE.
- seed
A numeric specifying the random seed. Default is 11.
Examples
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 11:53:45] Start standard scop workflow...
#> ℹ [2025-11-13 11:53:45] Checking a list of <Seurat> object...
#> ! [2025-11-13 11:53:46] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 11:53:46] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:53:48] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:53:48] Use the separate HVF from srt_list
#> ℹ [2025-11-13 11:53:48] Number of available HVF: 2000
#> ℹ [2025-11-13 11:53:48] Finished check
#> ℹ [2025-11-13 11:53:49] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 11:53:49] 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 11:53:50] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 11:53:50] Reorder clusters...
#> ℹ [2025-11-13 11:53:50] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 11:53:50] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:53:50] UMAP will return its model
#> ℹ [2025-11-13 11:53:54] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:53:54] UMAP will return its model
#> ✔ [2025-11-13 11:53:58] Run scop standard workflow done
pancreas_sub <- RunSlingshot(
pancreas_sub,
group.by = "SubCellType",
reduction = "UMAP"
)
CellDimPlot(
pancreas_sub,
group.by = "SubCellType",
reduction = "UMAP",
lineages = paste0("Lineage", 1:2),
lineages_span = 0.1
)
DynamicPlot(
pancreas_sub,
lineages = "Lineage1",
features = c("Arxes1", "Ncoa2", "G2M_score"),
group.by = "SubCellType",
compare_features = TRUE
)
#> ◌ [2025-11-13 11:54:01] Installing: MatrixGenerics...
#>
#> → Will install 1 package.
#> → The package (0 B) is cached.
#> + MatrixGenerics 1.22.0 [bld]
#>
#> ℹ No downloads are needed, 1 pkg is cached
#> ℹ Building MatrixGenerics 1.22.0
#> ✔ Built MatrixGenerics 1.22.0 (2.4s)
#> ✔ Installed MatrixGenerics 1.22.0 (1.1s)
#> ✔ 1 pkg + 1 dep: kept 1, added 1 [4.1s]
#> ✔ [2025-11-13 11:54:05] MatrixGenerics installed successfully
#> ℹ [2025-11-13 11:54:05] Start find dynamic features
#> ✔ [2025-11-13 11:54:05] mgcv installed successfully
#> ℹ [2025-11-13 11:54:05] Data type is raw counts
#> ℹ [2025-11-13 11:54:06] Number of candidate features (union): 3
#> ℹ [2025-11-13 11:54:06] Data type is raw counts
#> ! [2025-11-13 11:54:06] Negative values detected
#> ℹ [2025-11-13 11:54:06] Calculating dynamic features for "Lineage1"...
#> ℹ [2025-11-13 11:54:06] Using 1 core
#> ⠙ [2025-11-13 11:54:06] Running [1/3] ETA: 0s
#> ⠹ [2025-11-13 11:54:06] Running [2/3] ETA: 0s
#> ✔ [2025-11-13 11:54:06] Completed 3 tasks in 128ms
#>
#> ℹ [2025-11-13 11:54:06] Building results
#> ✔ [2025-11-13 11:54:07] Find dynamic features done
DynamicPlot(
pancreas_sub,
lineages = c("Lineage1", "Lineage2"),
features = c("Arxes1", "Ncoa2", "G2M_score"),
group.by = "SubCellType",
compare_lineages = TRUE,
compare_features = FALSE
)
#> ✔ [2025-11-13 11:54:07] MatrixGenerics installed successfully
#> ℹ [2025-11-13 11:54:07] Start find dynamic features
#> ✔ [2025-11-13 11:54:07] mgcv installed successfully
#> ℹ [2025-11-13 11:54:08] Data type is raw counts
#> ℹ [2025-11-13 11:54:08] Number of candidate features (union): 3
#> ℹ [2025-11-13 11:54:09] Data type is raw counts
#> ! [2025-11-13 11:54:09] Negative values detected
#> ℹ [2025-11-13 11:54:09] Calculating dynamic features for "Lineage1"...
#> ℹ [2025-11-13 11:54:09] Using 1 core
#> ℹ [2025-11-13 11:54:09] Building results
#> ✔ [2025-11-13 11:54:09] Find dynamic features done
#> ℹ [2025-11-13 11:54:09] Start find dynamic features
#> ✔ [2025-11-13 11:54:09] mgcv installed successfully
#> ℹ [2025-11-13 11:54:09] Data type is raw counts
#> ℹ [2025-11-13 11:54:10] Number of candidate features (union): 3
#> ℹ [2025-11-13 11:54:10] Data type is raw counts
#> ! [2025-11-13 11:54:10] Negative values detected
#> ℹ [2025-11-13 11:54:10] Calculating dynamic features for "Lineage2"...
#> ℹ [2025-11-13 11:54:10] Using 1 core
#> ⠙ [2025-11-13 11:54:10] Running [1/3] ETA: 0s
#> ✔ [2025-11-13 11:54:10] Completed 3 tasks in 128ms
#>
#> ℹ [2025-11-13 11:54:10] Building results
#> ✔ [2025-11-13 11:54:11] Find dynamic features done
DynamicPlot(
pancreas_sub,
lineages = c("Lineage1", "Lineage2"),
features = c("Arxes1", "Ncoa2", "G2M_score"),
group.by = "SubCellType",
compare_lineages = FALSE,
compare_features = FALSE
)
#> ✔ [2025-11-13 11:54:12] MatrixGenerics installed successfully
#> ℹ [2025-11-13 11:54:12] Start find dynamic features
#> ✔ [2025-11-13 11:54:12] mgcv installed successfully
#> ℹ [2025-11-13 11:54:12] Data type is raw counts
#> ℹ [2025-11-13 11:54:13] Number of candidate features (union): 3
#> ℹ [2025-11-13 11:54:14] Data type is raw counts
#> ! [2025-11-13 11:54:14] Negative values detected
#> ℹ [2025-11-13 11:54:14] Calculating dynamic features for "Lineage1"...
#> ℹ [2025-11-13 11:54:14] Using 1 core
#> ⠙ [2025-11-13 11:54:14] Running [1/3] ETA: 0s
#> ✔ [2025-11-13 11:54:14] Completed 3 tasks in 121ms
#>
#> ℹ [2025-11-13 11:54:14] Building results
#> ✔ [2025-11-13 11:54:14] Find dynamic features done
#> ℹ [2025-11-13 11:54:14] Start find dynamic features
#> ✔ [2025-11-13 11:54:14] mgcv installed successfully
#> ℹ [2025-11-13 11:54:14] Data type is raw counts
#> ℹ [2025-11-13 11:54:15] Number of candidate features (union): 3
#> ℹ [2025-11-13 11:54:15] Data type is raw counts
#> ! [2025-11-13 11:54:15] Negative values detected
#> ℹ [2025-11-13 11:54:15] Calculating dynamic features for "Lineage2"...
#> ℹ [2025-11-13 11:54:15] Using 1 core
#> ℹ [2025-11-13 11:54:15] Building results
#> ✔ [2025-11-13 11:54:15] Find dynamic features done
