Plotting a toy datasets using dynplot

Plotting the topology and cellular positions

If the topology is very simple (or should be represented in one dimension)

plot_onedim(trajectory)

## Coloring by milestone

## Using milestone_percentages from trajectory

## Loading required namespace: RColorBrewer

If the topology is a tree

plot_dendro(trajectory)

## Coloring by milestone

## Using milestone_percentages from trajectory

If the topology is more complex

plot_graph(trajectory)

## Coloring by milestone

## Using milestone_percentages from trajectory

plot_topology(trajectory, layout = "circle")

plot_dimred(trajectory)

## Coloring by milestone

## Using milestone_percentages from trajectory

plot_dimred(trajectory, color_trajectory = "nearest")

## Coloring by milestone
## Using milestone_percentages from trajectory

Plotting a grouping or clustering

grouping <- trajectory$prior_information$groups_id

plot_onedim(trajectory, grouping = grouping)

## Coloring by grouping

plot_dendro(trajectory, grouping = grouping)

## Coloring by grouping

plot_graph(trajectory, grouping = grouping)

## Coloring by grouping

plot_dimred(trajectory, grouping = grouping)

## Coloring by grouping

Plotting expression of one feature

feature_oi <- first(colnames(trajectory$counts))

plot_onedim(trajectory, feature_oi = feature_oi)

## Coloring by expression

plot_dendro(trajectory, feature_oi = feature_oi)

## Coloring by expression

plot_graph(trajectory, feature_oi = feature_oi)

## Coloring by expression

plot_dimred(trajectory, feature_oi = feature_oi)

## Coloring by expression

Plotting expression of a lot of features

plot_heatmap(trajectory)

## No features of interest provided, selecting the top 20 features automatically

## Using dynfeature for selecting the top 20 features

## Coloring by milestone

Comparing trajectories

pseudotime <- trajectory$counts %>% prcomp() %>% {.$x[, 1]}
prediction <- 
  dynwrap::wrap_data(id = "dummy_prediction", cell_ids = trajectory$cell_ids) %>%
  dynwrap::add_linear_trajectory(pseudotime)

plot_linearised_comparison(trajectory, prediction)

## root cell or milestone not provided, trying first outgoing milestone_id

## Using 'milestone_begin' as root

## Coloring by milestone

## Using milestone_percentages from trajectory

## root cell or milestone not provided, trying first outgoing milestone_id

## Using 'milestone_begin' as root

## Coloring by milestone

## Using milestone_percentages from trajectory