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Plot dynamic features across pseudotime

Source: R/DynamicPlot.R
DynamicPlot.Rd

Plot dynamic features across pseudotime

Usage

DynamicPlot(
  srt,
  lineages,
  features,
  group.by = NULL,
  group_use = 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 = "Chinese",
  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,
  cores = 1,
  verbose = TRUE,
  seed = 11,
  fit.by = NULL
)

Arguments

srt

A Seurat object.

lineages

Lineages to plot.

features

Features to use.

group.by

Metadata column(s) used to color cells.

group_use

Groups from group.by to keep. If both group_use and cells are provided, their intersection will be used.

fit.by

Optional metadata column used to fit an independent trajectory for each group over that group's observed pseudotime range. The fit is not extrapolated beyond observed cells. This does not change the existing point-coloring behavior of group.by. Groups with fewer than 10 cells or 10 unique pseudotime values are shown as raw points or rugs but are not fitted.

cells

Cell names to use.

layer

Assay layer to use.

assay

Assay to use. NULL uses the default assay.

family

GLM family. NULL chooses automatically.

exp_method

Expression transform: "log1p", "raw", "zscore", "fc", or "log2fc". With fit.by, transform parameters are shared across groups within each lineage.

lib_normalize

Library-size normalize. Defaults to TRUE when layer is counts.

libsize

Library size for each cell.

compare_lineages, compare_features

Compare lineages or features on one plot. Grouped line or interval plots support up to six combined lineage-feature series.

add_line, add_interval, line.size, line_palette, line_palcolor

Fitted line and CI.

add_point, pt.size, point_palette, point_palcolor

Overlay points.

add_rug

Draw rugs.

flip, reverse

Flip or reverse the x-axis.

x_order

Order of x-axis values.

aspect.ratio

Panel aspect ratio.

legend.position

Legend side ("right", "left", "top", "bottom"). Gap to the heatmap grows automatically when long row names are on the right.

legend.direction

Legend direction: "horizontal" or "vertical".

theme_use, theme_args

Theme name or function, plus extra theme arguments.

combine, nrow, ncol, byrow

Combine plots with patchwork. combine = FALSE returns a list of ggplots.

cores

Number of CPU cores.

verbose

Whether to print the message. Default is TRUE.

seed

Random seed.

See also

RunDynamicFeatures

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 04:24:10] Start standard processing workflow...
#> ℹ [2026-08-30 04:24:10] Checking a list of <Seurat>...
#> ! [2026-08-30 04:24:10] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:24:10] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:24:10] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:24:10] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:24:11] Number of available HVF: 2000
#> ℹ [2026-08-30 04:24:11] Finished check
#> ℹ [2026-08-30 04:24:11] Perform `ScaleData()`
#> ℹ [2026-08-30 04:24:11] Perform pca linear dimension reduction
#> ℹ [2026-08-30 04:24:11] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 04:24:11] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 04:24:11] Reorder clusters...
#> ℹ [2026-08-30 04:24:11] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:24:11] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 04:24:18] Standard processing workflow completed
pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP"
)
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 7 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
)


DynamicPlot(
  pancreas_sub,
  lineages = "Lineage1",
  features = c("Arxes1", "Ncoa2", "G2M_score"),
  group.by = "SubCellType",
  group_use = c("Ductal", "Beta"),
  compare_features = TRUE
)
#> ℹ [2026-08-30 04:24:20] Start find dynamic features
#> ℹ [2026-08-30 04:24:21] Data type is raw counts
#> ℹ [2026-08-30 04:24:21] Number of candidate features (union): 3
#> ℹ [2026-08-30 04:24:21] Data type is raw counts
#> ! [2026-08-30 04:24:21] Negative values detected
#> Warning: Negative values detected
#> ℹ [2026-08-30 04:24:21] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:24:21] Using 1 core
#> ⠙ [2026-08-30 04:24:21] Running for Arxes1 [1/3] ■■■         33% | ETA:  0s
#> ✔ [2026-08-30 04:24:21] Completed 3 tasks in 104ms
#> 
#> ℹ [2026-08-30 04:24:21] Building results
#> ✔ [2026-08-30 04:24:21] Find dynamic features done


# Demonstration only: create two conditions spanning the same pseudotime.
lineage1_cells <- rownames(pancreas_sub@meta.data)[
  order(pancreas_sub$Lineage1, na.last = NA)
]
pancreas_sub$condition <- NA_character_
pancreas_sub$condition[lineage1_cells] <- rep(
  c("control", "HUA"),
  length.out = length(lineage1_cells)
)
DynamicPlot(
  pancreas_sub,
  lineages = "Lineage1",
  features = "Vim",
  group.by = "condition",
  fit.by = "condition"
)
#> ℹ [2026-08-30 04:24:22] Start find dynamic features
#> ℹ [2026-08-30 04:24:23] Data type is raw counts
#> ℹ [2026-08-30 04:24:23] Number of candidate features (union): 1
#> ℹ [2026-08-30 04:24:23] Data type is raw counts
#> ℹ [2026-08-30 04:24:23] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:24:23] Using 1 core
#> ℹ [2026-08-30 04:24:23] Building results
#> ✔ [2026-08-30 04:24:23] Find dynamic features done
#> ℹ [2026-08-30 04:24:23] Start find dynamic features
#> ℹ [2026-08-30 04:24:24] Data type is raw counts
#> ℹ [2026-08-30 04:24:24] Number of candidate features (union): 1
#> ℹ [2026-08-30 04:24:24] Data type is raw counts
#> ℹ [2026-08-30 04:24:24] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:24:24] Using 1 core
#> ℹ [2026-08-30 04:24:24] Building results
#> ✔ [2026-08-30 04:24:24] 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
)
#> ℹ [2026-08-30 04:24:25] Start find dynamic features
#> ℹ [2026-08-30 04:24:25] Data type is raw counts
#> ℹ [2026-08-30 04:24:25] Number of candidate features (union): 3
#> ℹ [2026-08-30 04:24:25] Data type is raw counts
#> ! [2026-08-30 04:24:25] Negative values detected
#> Warning: Negative values detected
#> ℹ [2026-08-30 04:24:25] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:24:25] Using 1 core
#> ⠙ [2026-08-30 04:24:25] Running for Arxes1 [1/3] ■■■         33% | ETA:  0s
#> ✔ [2026-08-30 04:24:25] Completed 3 tasks in 104ms
#> 
#> ℹ [2026-08-30 04:24:25] Building results
#> ✔ [2026-08-30 04:24:26] Find dynamic features done
#> ℹ [2026-08-30 04:24:26] Start find dynamic features
#> ℹ [2026-08-30 04:24:26] Data type is raw counts
#> ℹ [2026-08-30 04:24:26] Number of candidate features (union): 3
#> ℹ [2026-08-30 04:24:27] Data type is raw counts
#> ! [2026-08-30 04:24:27] Negative values detected
#> Warning: Negative values detected
#> ℹ [2026-08-30 04:24:27] Calculating dynamic features for "Lineage2"...
#> ℹ [2026-08-30 04:24:27] Using 1 core
#> ℹ [2026-08-30 04:24:27] Building results
#> ✔ [2026-08-30 04:24:27] 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
)
#> ℹ [2026-08-30 04:24:28] Start find dynamic features
#> ℹ [2026-08-30 04:24:28] Data type is raw counts
#> ℹ [2026-08-30 04:24:28] Number of candidate features (union): 3
#> ℹ [2026-08-30 04:24:29] Data type is raw counts
#> ! [2026-08-30 04:24:29] Negative values detected
#> Warning: Negative values detected
#> ℹ [2026-08-30 04:24:29] Calculating dynamic features for "Lineage1"...
#> ℹ [2026-08-30 04:24:29] Using 1 core
#> ⠙ [2026-08-30 04:24:29] Running for Arxes1 [1/3] ■■■         33% | ETA:  0s
#> ✔ [2026-08-30 04:24:29] Completed 3 tasks in 106ms
#> 
#> ℹ [2026-08-30 04:24:29] Building results
#> ✔ [2026-08-30 04:24:29] Find dynamic features done
#> ℹ [2026-08-30 04:24:29] Start find dynamic features
#> ℹ [2026-08-30 04:24:29] Data type is raw counts
#> ℹ [2026-08-30 04:24:29] Number of candidate features (union): 3
#> ℹ [2026-08-30 04:24:30] Data type is raw counts
#> ! [2026-08-30 04:24:30] Negative values detected
#> Warning: Negative values detected
#> ℹ [2026-08-30 04:24:30] Calculating dynamic features for "Lineage2"...
#> ℹ [2026-08-30 04:24:30] Using 1 core
#> ℹ [2026-08-30 04:24:30] Building results
#> ✔ [2026-08-30 04:24:30] Find dynamic features done