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dynplot: Plotting Single-Cell Trajectories

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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 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")
<img src="man/figures/dendro-1.png" width="100%" />

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")
<img src="man/figures/onedim-1.png" width="100%" />

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")
<img src="man/figures/graph-1.png" width="100%" />

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")
<img src="man/figures/dimred-1.png" width="100%" />

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)
<img src="man/figures/heatmap-1.png" width="100%" />

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.

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trajectory$id <- "Bifurcating"
prediction$id <- "Linear"
plot_linearised_comparison(trajectory, prediction)
<img src="man/figures/scatterplot-1.png" width="100%" /> <!-- TODO: create dedicated function for this ## Alternative: Compare two trajectories by projecting dimensionality reductions You can also use the colouring of the cells in order to compare two trajectories. {r dimredcompare} traj1 <- trajectory %>% dynwrap::add_dimred(dimred = dyndimred::dimred_landmark_mds) traj2 <- prediction %>% dynwrap::add_dimred(dimred = dyndimred::dimred_landmark_mds, expression_source = trajectory) plot_dimred(traj1, milestone_percentages = traj2$milestone_percentages, milestones = traj2$milestone_ids) plot_dimred( traj2, expression_source = traj1, color_cells = "milestone", milestones = traj1$milestone_ids, milestone_percentages = traj1$milestone_percentages ) -->

Latest changes

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

<!-- This section gets automatically generated from inst/NEWS.md -->

Recent changes in dynplot 1.1.1

Recent changes in dynplot 1.1.0

Initial release on CRAN.