dynplot: Plotting Single-Cell Trajectories

ℹ️ Tutorialsℹ️ 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.

Here’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 informationgrouping<-trajectory$prior_information$groups_idgroups<- 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#> a9724dff2655: Pulling fs layer#> f5f3a048c9c3: Pulling fs layer#> a53c4db932de: Pulling fs layer#> bd5da474b8ba: Pulling fs layer#> be9b916e18e7: Pulling fs layer#> b8b469f67972: Pulling fs layer#> e33132a1a81b: Pulling fs layer#> 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#> b8b469f67972: Waiting#> f5f3a048c9c3: Waiting#> bd5da474b8ba: Waiting#> d97023e2f782: Waiting#> 844c33c7e6ea: Verifying Checksum#> 844c33c7e6ea: Download complete#> 844c33c7e6ea: Pull complete#> f5f3a048c9c3: Verifying Checksum#> f5f3a048c9c3: Download complete#> a53c4db932de: Verifying Checksum#> a53c4db932de: Download complete#> bd5da474b8ba: Verifying Checksum#> bd5da474b8ba: Download complete#> 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:v0.9.9.01trajectory$id<-"Bifurcating"prediction$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 “Must supply a symbol or a string as argument” (#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() wouldn’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.