ComplexUpset R Package

repository·master·Indexed 20 days ago

https://github.com/krassowski/complex-upset

An R package for creating highly extensible UpSet visualizations using ggplot2 and patchwork. It allows users to add complex annotations (such as boxplots or violin plots) and queries to highlight specific intersections or sets, providing more flexibility than UpSetR. The package supports installation via CRAN, GitHub, and conda-forge, and includes integration guides for using R within Jupyter Notebooks via rpy2.

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What's inside ComplexUpset

How ComplexUpset compares to other UpSet packages

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Choosing the right tool depends on your specific needs:

Package	Comparison
ComplexUpset	Provides full `ggplot2` extensibility using `patchwork`. Best for highly customized, annotated plots.
UpSetR	The pioneer tool, but no longer actively maintained and not designed for `ggplot2` extension.
ggupset	Uses scales (e.g., `scale_x_upset`) to convert plots. Recommended for simple `ggplot` workflows.
ComplexHeatmap	Offers UpSet plots with annotations. Best if you are already using it for heatmap workflows.
VCG/upset	An option for interactive use.

Install ComplexUpset
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You can install ComplexUpset using several methods depending on your environment:
From GitHub (Latest Version)
Use devtools to install the most recent development version:
```
if(!require(devtools)) install.packages("devtools")
devtools::install_github("krassowski/complex-upset")
```
From CRAN (Stable Release)
Install the stable version available on CRAN:
```
install.packages('ComplexUpset')
```
Using Conda/Mamba
Install via the conda-forge channel:
```
conda install -c conda-forge r-complexupset
```
```
if(!require(devtools)) install.packages("devtools")
devtools::install_github("krassowski/complex-upset")
```
https://github.com/krassowski/complex-upset/blob/master/README.md
Install Python requirements for ComplexUpset
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To use ComplexUpset within a Jupyter Notebook via Python integration, you must install pandas, pydataset, and rpy2 in your Python environment.
```
pip install pandas pydataset rpy2
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_Python.ipynb
Prepare datasets for ComplexUpset
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ComplexUpset typically operates on datasets where set membership is represented by columns. If your dataset uses integer indicators (e.g., 1 for presence, 0 for absence), you should convert these columns to boolean values (TRUE/FALSE) to ensure compatibility with the package's logic.
Example workflow:
1. Identify the columns representing the sets (e.g., genres).
2. Convert those columns to boolean: df[set_columns] = df[set_columns] == 1.
3. Ensure there are no missing values in the columns used for set membership or grouping if using na.omit().
```
# Example: Converting genre columns to boolean
genres = colnames(movies)[18:24]
movies[genres] = movies[genres] == 1
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_R.ipynb
Import Python DataFrames to R using rpy2 magic
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You can pass Python objects (like Pandas DataFrames) into the R environment using the %R -i switch. This is necessary to use your prepared Python data for ComplexUpset plotting in R.
```
# Example: Importing 'movies' and 'genres' DataFrames into R
%R -i movies -i genres
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_Python.ipynb
Install ComplexUpset from GitHub via R magic
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If ComplexUpset is not already installed in your R environment, you can install it directly from the GitHub repository using the %%R magic command. This step also ensures devtools is available.
```
%%R
if (!require(devtools, quietly=T)) install.packages("devtools")
if (!require(ComplexUpset, quietly=T)) devtools::install_github("krassowski/complex-upset")
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_Python.ipynb
Set up R integration in Jupyter Notebooks
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After installing the requirements, load the rpy2 IPython extension in your notebook to enable the %%R magic command. This allows you to run R code directly within Python cells.
```
%%capture
%load_ext rpy2.ipython
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_Python.ipynb
Configure plot output size in Jupyter notebooks
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If you are working in a Jupyter notebook using the IRKernel, you can use a utility function to adjust the output dimensions of your plots. This is not required if you are using RStudio or running R from a terminal.
```
set_size = function(w, h, factor=1.5) {
    s = 1 * factor
    options(
        repr.plot.width=w * s,
        repr.plot.height=h * s,
        repr.plot.res=100 / factor,
        jupyter.plot_mimetypes='image/png',
        jupyter.plot_scale=1
    )
}
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_R.ipynb
Install and load ComplexUpset and ggplot2
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To use ComplexUpset, you must have ggplot2 and ComplexUpset installed in your R environment. Load them using library() before performing any plotting operations.
```
library(ggplot2)
library(ComplexUpset)
```
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_R.ipynb

Create an UpSet plot with annotations and queries

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The upset() function is the primary interface for generating UpSet plots. It allows you to extend UpSetR functionality with ggplot2's extensibility.

Key features include:

annotations: A list of ggplot2 objects that add data visualizations (like boxplots or violin plots) corresponding to the intersections.
queries: A list of upset_query() objects used to highlight specific intersections or sets with custom colors and fills.
min_size: Filters intersections by a minimum size.
width_ratio: Adjusts the width ratio of the plot components.

Note: If you use ggbeeswarm::geom_quasirandom in your annotations, ensure the ggbeeswarm package is installed; otherwise, you can use geom_jitter from ggplot2.

library(ggplot2)
library(ComplexUpset)

if(!require(ggplot2movies)) install.packages('ggplot2movies')
movies = ggplot2movies::movies
genres = c('Action', 'Animation', 'Comedy', 'Drama', 'Documentary', 'Romance')

upset(
    movies,
    genres,
    annotations = list(
        'Length'=ggplot(mapping=aes(x=intersection, y=length)) + geom_boxplot(),
        'Rating'=ggplot(mapping=aes(x=intersection, y=rating))
            + ggbeeswarm::geom_quasirandom(aes(color=log10(votes)))
            + geom_violin(width=1.1, alpha=0.5)
    ),
    queries=list(
        upset_query(
            intersect=c('Drama', 'Comedy'),
            color='red',
            fill='red',
            only_components=c('intersections_matrix', 'Intersection size')
        ),
        upset_query(
            set='Drama',
            fill='blue'
        ),
        upset_query(
            intersect=c('Romance', 'Drama'),
            fill='yellow',
            only_components=c('Length')
        )
    ),
    min_size=10,
    width_ratio=0.1
)

Configure R magic plot output settings
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When using the %%R magic command to generate plots, you can use specific switches to control the output:
- -i: Import data from the Python environment into R.
- -w: Adjust the width of the plot.
- -h: Adjust the height of the plot.
- -r: Adjust the DPI (dots per inch) of the plot.
https://github.com/krassowski/complex-upset/blob/master/vignettes/Prepare_Python.ipynb

ComplexUpset R Package

What's inside ComplexUpset

From GitHub (Latest Version)

From CRAN (Stable Release)

Using Conda/Mamba