# Plotting a toy datasets using dynplot

#### 2021-12-07

Load in a toy dataset

    data(example_bifurcating)
    trajectory <- example_bifurcating

## 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