https://github.com/dynverse/dynplot

Visualising single-cell trajectories, including comparisons between two models 📈
https://github.com/dynverse/dynplot

trajectory-inference visualisation

Last synced: 8 months ago
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Visualising single-cell trajectories, including comparisons between two models 📈

• Host: GitHub
• URL: https://github.com/dynverse/dynplot
• Owner: dynverse
• License: other
• Created: 2017-08-22T11:24:51.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2021-12-09T08:15:45.000Z (over 4 years ago)
• Last Synced: 2025-10-22T04:53:02.591Z (8 months ago)
• Topics: trajectory-inference, visualisation
• Language: R
• Homepage:
• Size: 36.1 MB
• Stars: 31
• Watchers: 1
• Forks: 5
• Open Issues: 10
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

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README

          
---

title: "dynplot: Plotting Single-Cell Trajectories"

output:

  github_document:

    html_preview: false

editor_options: 

  chunk_output_type: console

---

```{r setup1, include=FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  out.width = "100%",

  fig.path = "man/figures/",

  message = FALSE,

  dpi = 300

)

set.seed(1)

```

 

  

[**ℹ️ Tutorials**](https://dynverse.org)       

[**ℹ️ Reference documentation**](https://dynverse.org/reference/dynplot) 


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.

```{r setup, warning=FALSE}

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

```{r dendro, fig.width=8, fig.height=6}

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

```{r onedim, fig.width=8, fig.height=8}

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

```{r graph, fig.width=8, fig.height=5}

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

```{r dimred, fig.width=8, fig.height=5}

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.

```{r heatmap, fig.width=10, fig.height=6}

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.

```{r scatterplot, fig.width=8, fig.height=4}

prediction <- infer_trajectory(trajectory, ti_comp1())

trajectory$id <- "Bifurcating"

prediction$id <- "Linear"

plot_linearised_comparison(trajectory, prediction)

```

## Latest changes

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

```{r news, echo=FALSE, results="asis"}

cat(dynutils::recent_news())

```