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https://github.com/iSEE/iSEE
R/shiny interface for interactive visualization of data in SummarizedExperiment objects
https://github.com/iSEE/iSEE
clustering dimension-reduction feature-extraction gene-expression gui hacktoberfest human-cell-atlas shiny single-cell transcriptomics visualization
Last synced: 8 days ago
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R/shiny interface for interactive visualization of data in SummarizedExperiment objects
- Host: GitHub
- URL: https://github.com/iSEE/iSEE
- Owner: iSEE
- License: other
- Created: 2017-12-06T15:28:42.000Z (about 7 years ago)
- Default Branch: devel
- Last Pushed: 2024-12-01T14:15:58.000Z (11 days ago)
- Last Synced: 2024-12-01T14:28:04.990Z (11 days ago)
- Topics: clustering, dimension-reduction, feature-extraction, gene-expression, gui, hacktoberfest, human-cell-atlas, shiny, single-cell, transcriptomics, visualization
- Language: R
- Homepage: https://isee.github.io/iSEE/
- Size: 18.4 MB
- Stars: 224
- Watchers: 18
- Forks: 42
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - iSEE/iSEE - R/shiny interface for interactive visualization of data in SummarizedExperiment objects (R)
README
# _iSEE_ - The interactive SummarizedExperiment Explorer
[](https://github.com/iSEE/iSEE/issues)
[](https://github.com/iSEE/iSEE/pulls)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/iSEE/iSEE/actions)
[](https://app.codecov.io/gh/iSEE/iSEE?branch=main)
## Bioconductor release status
| Branch | R CMD check | Last updated |
|:----------------:|:----------------:|:------------:|
| [_devel_](http://bioconductor.org/packages/devel/bioc/html/iSEE.html) | [](http://bioconductor.org/checkResults/devel/bioc-LATEST/iSEE) |  |
| [_release_](http://bioconductor.org/packages/release/bioc/html/iSEE.html) | [](http://bioconductor.org/checkResults/release/bioc-LATEST/iSEE) |  |
## Overview
The _iSEE_ package provides an interactive user interface for exploring data in objects derived from the `SummarizedExperiment` class.
Particular focus is given to single-cell data stored in the `SingleCellExperiment` derived class.
The user interface is implemented with [RStudio](https://www.rstudio.com)'s [_Shiny_](https://shiny.rstudio.com), with a multi-panel setup for ease of navigation.
This initiative was proposed at the European Bioconductor Meeting in Cambridge, 2017.
Current contributors include:
- [Charlotte Soneson](https://github.com/csoneson)
- [Aaron Lun](https://github.com/LTLA)
- [Federico Marini](https://github.com/federicomarini)
- [Kévin Rue-Albrecht](https://github.com/kevinrue)
[![Figure 1. _iSEE_ uses a customisable multi-panel layout.][Figure1]](https://f1000research.com/articles/7-741/v1)
## Installation
_iSEE_ can be easily installed from Bioconductor using `BiocManager::install()`:
```r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("iSEE")
# or also...
BiocManager::install("iSEE", dependencies = TRUE)
```
Setting `dependencies = TRUE` should ensure that all packages, including the ones in the `Suggests:` field of the `DESCRIPTION`, are installed - this can be essential if you want to reproduce the code in the vignette, for example.
## Functionalities
Click to expand the list of features available in iSEE applications.
### General
* Multiple interactive plot types with selectable points.
* Interactive tables with selectable rows.
* Coloring of samples and features by metadata or expression data.
* Zooming to a plot subregion.
* Transmission of point selections between panels to highlight, color, or restrict data points in the receiving panel(s).
* Lasso point selection to define complex shapes.
### Sample-level visualization
The _iSEE_ user interface currently contains the following components where each data point represents a single biological sample:
* **Reduced dimension plot**: Scatter plot of reduced dimensionality data.
* **Column data plot**: Adaptive plot of any one or two sample metadata.
A scatter, violin, or square design is dynamically applied according to the continuous or discrete nature of the metadata.
* **Feature assay plot**: Adaptive plot of expression data across samples for any two features or one feature against one sample metadata.
* **Column data table**: Table of sample metadata.
### Feature-level visualization
The _iSEE_ user interface currently contains the following components where each data point represents a genomic feature:
* **Row data plot**: Adaptive plot of any two feature metadata.
A scatter, violin, or square design is dynamically applied according to the continuous or discrete nature of the metadata.
* **Sample assay plot**: Adaptive plot of expression data across features for any two samples or one sample against one feature metadata.
* **Row data table**: Table of feature metadata.
### Integrated visualization
The _iSEE_ user interface contains the following components that integrate sample and feature information:
* **Complex heatmap plot**: Visualize multiple features across multiple samples annotated with sample metadata.
### Custom panels
The _iSEE_ API allows users to programmatically define their own plotting and table panels.
See the section [Extending _iSEE_](#extending-isee) further below.
### Miscellaneous
* The _iSEE_ user interface continually tracks the code corresponding to all visible plotting panels.
This code is rendered in a [shinyAce](https://cran.r-project.org/web/packages/shinyAce/index.html) text editor and can be copy-pasted into R scripts for customization and further use.
* Speech recognition can be enabled to control the user interface using voice commands.
## Want to try _iSEE_?
We set up instances of _iSEE_ applications running on diverse types of datasets at those addresses:
- http://shiny.imbei.uni-mainz.de:3838/iSEE
- https://marionilab.cruk.cam.ac.uk/iSEE_allen
- https://marionilab.cruk.cam.ac.uk/iSEE_tcga
- https://marionilab.cruk.cam.ac.uk/iSEE_pbmc4k
- https://marionilab.cruk.cam.ac.uk/iSEE_cytof
Please keep in mind that those public instances are for trial purposes only;
yet they demonstrate how you or your system administrator can setup _iSEE_ for analyzing or sharing your precomputed `SummarizedExperiment`/`SingleCellExperiment` object.
## Extending _iSEE_
If you want to extend the functionality of _iSEE_, you can create custom panels which add new possibilities to interact with your data.
Custom panels can be defined in independent R packages that include _iSEE_ in the `Imports:` sections of their DESCRIPTION file.
You can find a collection of working examples of how to do it in [iSEEu](https://github.com/iSEE/iSEEu).
Feel free to contact the developing team, should you need some clarifications on how _iSEE_ works internally.
[Figure1]: https://f1000researchdata.s3.amazonaws.com/manuscripts/16293/6bf85f9d-8352-4a78-a8da-456f05f5c4c9_figure1.gif "iSEE uses a customisable multi-panel layout"
## Code of Conduct
Please note that the iSEE project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.