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dynplot: Plotting Single-Cell Trajectories


โ„น๏ธ Tutorials ย  ย  โ„น๏ธ Reference documentation

Visualise a single-cell trajectory as a graph or dendrogram, as a dimensionality reduction or heatmap of the expression data, or a comparison between two trajectories as a pairwise scatterplot or dimensionality reduction projection.

Hereโ€™s a summary of the different plotting functions for visualising single-cell trajectories.

library(tidyverse)
library(dyno)

# get trajectory
data(example_bifurcating)

trajectory <- example_bifurcating %>% add_root()

# gather some prior information
grouping <- trajectory$prior_information$groups_id

groups <- tibble(
  group_id = trajectory$milestone_ids,
  color = dynplot:::milestone_palette_list$auto(length(group_id))
)
features_oi <- apply(as.matrix(trajectory$counts), 2, sd) %>% sort() %>% names() %>% tail(10)
feature_oi <- features_oi[[10]]

plot_dendro(): Plot a trajectory as a dendrogram

patchwork::wrap_plots(
  plot_dendro(trajectory) + labs(title = "Topology"),
  plot_dendro(trajectory, "milestone") + labs(title = "Ordering"),
  plot_dendro(trajectory, grouping=grouping, groups=groups) + labs(title = "Grouping/clustering"),
  plot_dendro(trajectory, feature_oi=feature_oi) + labs(title = "Expression of\na single gene"),
  plot_dendro(trajectory, "pseudotime") + labs(title = "Pseudotime"),
  byrow = TRUE,
  ncol = 3
) & theme(legend.position = "none")

plot_onedim(): Plot a trajectory as a one-dimensional set of connected segments

patchwork::wrap_plots(
  plot_onedim(trajectory) + labs(title = "Topology"),
  plot_onedim(trajectory, "milestone") + labs(title = "Ordering"),
  plot_onedim(trajectory, grouping=grouping, groups=groups) + labs(title = "Grouping/clustering"),
  plot_onedim(trajectory, feature_oi=feature_oi) + labs(title = "Expression of\na single gene"),
  plot_onedim(trajectory, "pseudotime") + labs(title = "Pseudotime"),
  byrow = TRUE,
  ncol = 2
) & theme(legend.position = "none")

plot_graph(): Plot a trajectory and cellular positions as a graph

patchwork::wrap_plots(
  plot_graph(trajectory) + labs(title = "Topology"),
  plot_graph(trajectory, "milestone") + labs(title = "Ordering"),
  plot_graph(trajectory, grouping=grouping, groups=groups) + labs(title = "Grouping/clustering"),
  plot_graph(trajectory, feature_oi=feature_oi) + labs(title = "Expression of\na single gene"),
  plot_graph(trajectory, "pseudotime") + labs(title = "Pseudotime"),
  byrow = TRUE,
  ncol = 3
) & theme(legend.position = "none")

plot_dimred(): Plot a trajectory in a (given) dimensionality reduction

patchwork::wrap_plots(
  plot_dimred(trajectory) + labs(title = "Topology"),
  plot_dimred(trajectory, "milestone") + labs(title = "Ordering"),
  plot_dimred(trajectory, grouping=grouping, groups=groups) + labs(title = "Grouping/clustering"),
  plot_dimred(trajectory, feature_oi=feature_oi) + labs(title = "Expression of\na single gene"),
  plot_dimred(trajectory, "pseudotime") + labs(title = "Pseudotime"),
  byrow = TRUE,
  ncol = 3
) & theme(legend.position = "none")

plot_heatmap(): Plot expression data along a trajectory

In addition, you can also plot the expression of genes along the trajectory as a heatmap.

plot_heatmap(trajectory, grouping = trajectory$prior_information$grouping_assignment)

plot_linearised_comparison(): Compare two trajectories as a pseudotime scatterplot

You can compare multiple trajectories (for the same cells) by creating a scatterplot between the two trajectories.

prediction <- infer_trajectory(trajectory, ti_comp1())
#> v0.9.9.01: Pulling from dynverse/ti_comp1
#> 844c33c7e6ea: Pulling fs layer
#> 4f11e4e30170: Pulling fs layer
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#> be9b916e18e7: Pulling fs layer
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#> 32bd550d2fc1: Pulling fs layer
#> a828ddf00b38: Pulling fs layer
#> d97023e2f782: Pulling fs layer
#> 84d8340a282e: Pulling fs layer
#> be9b916e18e7: Waiting
#> e33132a1a81b: Waiting
#> 32bd550d2fc1: Waiting
#> a828ddf00b38: Waiting
#> a53c4db932de: Waiting
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#> f5f3a048c9c3: Waiting
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#> 844c33c7e6ea: Verifying Checksum
#> 844c33c7e6ea: Download complete
#> 844c33c7e6ea: Pull complete
#> f5f3a048c9c3: Verifying Checksum
#> f5f3a048c9c3: Download complete
#> a53c4db932de: Verifying Checksum
#> a53c4db932de: Download complete
#> bd5da474b8ba: Verifying Checksum
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#> be9b916e18e7: Download complete
#> 4f11e4e30170: Verifying Checksum
#> 4f11e4e30170: Download complete
#> 4f11e4e30170: Pull complete
#> e33132a1a81b: Verifying Checksum
#> e33132a1a81b: Download complete
#> a9724dff2655: Verifying Checksum
#> a9724dff2655: Download complete
#> 32bd550d2fc1: Verifying Checksum
#> 32bd550d2fc1: Download complete
#> a9724dff2655: Pull complete
#> f5f3a048c9c3: Pull complete
#> a53c4db932de: Pull complete
#> bd5da474b8ba: Pull complete
#> be9b916e18e7: Pull complete
#> a828ddf00b38: Verifying Checksum
#> a828ddf00b38: Download complete
#> 84d8340a282e: Verifying Checksum
#> 84d8340a282e: Download complete
#> d97023e2f782: Download complete
#> b8b469f67972: Verifying Checksum
#> b8b469f67972: Download complete
#> b8b469f67972: Pull complete
#> e33132a1a81b: Pull complete
#> 32bd550d2fc1: Pull complete
#> a828ddf00b38: Pull complete
#> d97023e2f782: Pull complete
#> 84d8340a282e: Pull complete
#> Digest: sha256:012b3bfef2250767bcd3f3b389ebff82d4a2dc23d39b1bdadf7076e23b833680
#> Status: Downloaded newer image for dynverse/ti_comp1:v0.9.9.01
#> docker.io/dynverse/ti_comp1:v0.9.9.01

trajectory$id <- "Bifurcating"
prediction$id <- "Linear"
plot_linearised_comparison(trajectory, prediction)

Latest changes

Check out news(package = "dynwrap") or NEWS.md for a full list of changes.

Recent changes in dynplot 1.1.1

  • BUG FIX project_waypoints_coloured(): Fix refactoring issue โ€œMust supply a symbol or a string as argumentโ€ (#54).

  • BUG FIX project_waypoints_coloured(): Fix wrong results when projecting waypoint segments (#54 bis).

Recent changes in dynplot 1.1.0

Initial release on CRAN.

  • MINOR CHANGE: Add arrow parameter to all plot functions.

  • BUG FIX: Apply fixes for new versions of tibble, tidyr, and ggraph.

  • BUG FIX optimise_order(): Fix problem where GA::ga() wouldnโ€™t run on milestone networks with 1 or 4 edges.

  • BUG FIX linearise_cells(): Fix ordering issue when equal_cell_width is TRUE.

  • MINOR CHANGE: Clean imports and supposed undefined variables.

  • MINOR CHANGE plot_dendro(): Allow plotting of disconnected graphs (#32). This assumes that dynwrap::add_root(traj, root_milestone_id = c(...)) has been called properly.

  • DOCUMENTATION: Extend documentation on usage of parameters and the expected output values of functions.

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Visualising single-cell trajectories, including comparisons between two models ๐Ÿ“ˆ

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