1 Introduction

iSEEhub is an extension to the iSEE Bioconductor package that provides a link between iSEE and the Bioconductor ExperimentHub.

Specifically, iSEEhub adds a custom landing page to iSEE web applications that can be used to browse the Bioconductor ExperimentHub interactively, and loads a selected data set in the standard iSEE user interface.

2 Basics

2.1 Install iSEEhub

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. iSEEhub is a R package available via the Bioconductor repository for packages. R can be installed on any operating system from CRAN after which you can install iSEEhub by using the following commands in your R session:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
      install.packages("BiocManager")
  }

BiocManager::install("iSEEhub")

## Check that you have a valid Bioconductor installation
BiocManager::valid()

2.2 Required knowledge

iSEEhub is based on many other packages that have implemented the infrastructure needed for dealing with omics data and interactive visualisation. That is, packages like SummarizedExperiment, SingleCellExperiment, iSEE and shiny.

If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.

2.3 Asking for help

As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But R and Bioconductor have a steep learning curve so it is critical to learn where to ask for help. The blog post quoted above mentions some but we would like to highlight the Bioconductor support site as the main resource for getting help: remember to use the iSEEhub tag and check the older posts. Other alternatives are available such as creating GitHub issues and tweeting. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.

2.4 Citing iSEEhub

We hope that iSEEhub will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

## Citation info
citation("iSEEhub")
#> To cite package 'iSEEhub' in publications use:
#> 
#>   Rue-Albrecht K (2023). _iSEEhub: iSEE for the Bioconductor ExperimentHub_. doi:10.18129/B9.bioc.iSEEhub
#>   <https://doi.org/10.18129/B9.bioc.iSEEhub>, R package version 1.2.1,
#>   <https://bioconductor.org/packages/iSEEhub>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {iSEEhub: iSEE for the Bioconductor ExperimentHub},
#>     author = {Kevin Rue-Albrecht},
#>     year = {2023},
#>     note = {R package version 1.2.1},
#>     url = {https://bioconductor.org/packages/iSEEhub},
#>     doi = {10.18129/B9.bioc.iSEEhub},
#>   }

Here is an example of you can cite your package inside the vignette:

3 Quick start to using to iSEEhub

The main functionality of the package is available through the function iSEEhub().

The function returns a shiny app that can then be launched using the function shiny::runApp().

library(iSEEhub)
library(ExperimentHub)
ehub <- ExperimentHub()

app <- iSEEhub(ehub)

if (interactive()) {
  shiny::runApp(app, port = 1234)
}

4 The ExperimentHub pane

Datasets available in the Bioconductor ExperimentHub are listed – along with metadata – in the interactive table on the left.

The table may be filtered and sorted using any metadata column, to efficiently browse the datasets available.

By default, only a subset of metadata columns are displayed. The selectize input labelled Show columns: at the top of the pane may be used to add, remove, or reorder columns in the table.

No more than one dataset may be selected at any time.

5 The Selected Dataset pane

5.1 Overview

The pane on the right updates with the currently selected dataset.

This pane contains information and inputs required to load the currently selected dataset in the main iSEE app.

The pane is composed of two tabs described in the following sections.

5.2 The Info tab

The Info tab displays the full metadata associated with the currently selected dataset – the same metadata as in the ExperimentHub table – in formatted text.

As such, users can uses a minimal subset of columns in the table to efficiently browse available datasets, while having a full overview of the currently selected dataset in this tab.

5.3 The Config tab

The Config tab displays a selectize input offering users a choice of initial app configurations that are specific to the currently selected dataset.

For all datasets, an option Default is available. That option does not provide any specific instruction with respect to the initial set of panels, their layout, nor their respective initial settings. Instead, the Default option prompts the app to automatically identify all the built-in iSEE panel classes that are compatible with the loaded dataset, initialising an app that contains one instance of each of those panels.

The number of panels and data points to draw for large datasets makes the Default option a showcase mode for demonstration and for new users, more than one optimised for a short loading time or featuring specific aspects of the dataset.

For some datasets, additional choices of initial settings are available. Configurations are provided as scripts that define the list of panels and their respective initial configuration when the main iSEE app is launched.

To contribute new scripts, please refer to the vignette Contributing to iSEEhub.

For demonstration, a simple example config_1.R is included for the dataset EH1. When selected, the contents of the configuration file are shown in the pane – including any comment from the authors – allowing users to review the script before using it.

When the appropriate configuration is selected, users may click the Launch! button to load the dataset into the main iSEE app.

6 The main iSEE app

Once the dataset is successfully loaded into the R session, the app will switch to the main iSEE view.

7 Managing dataset dependencies

7.1 Overview

Each dataset in the Bioconductor ExperimentHub is associated with package dependencies that are required to handle the dataset in an R session. Package dependencies must be installed in the R library before the corresponding dataset can be loaded.

The list of packages associated with all datasets types supported by iSEEhub is regularly updated in the Suggests: field of the DESCRIPTION file. However, those packages are not automatically installed in a minimal installation of the iSEEhub package.

7.2 From the R console

By default, iSEEhub(..., runtime_install = FALSE) prevents the app from installing those dependencies at runtime. In that case, when users identify a missing dependency, they must interrupt the app, install the required package(s) using the R console, and re-launch the app. We recommend this approach for applications hosted on public web-servers.

7.3 From the live app

Alternatively, iSEEhub(..., runtime_install = TRUE) can be used to launch an app that prompts users interactively (i.e., within the app) for their consent to install missing dependencies. We recommend this approach for applications run on personal computers (unless users prefer to install packages themselves, of course).

8 Reproducibility

The iSEEhub package (Rue-Albrecht, 2023) was made possible thanks to:

This package was developed using biocthis.

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("iSEEhub.Rmd", "BiocStyle::html_document"))

## Extract the R code
library("knitr")
knit("iSEEhub.Rmd", tangle = TRUE)

Date the vignette was generated.

#> [1] "2023-07-05 17:09:13 EDT"

Wallclock time spent generating the vignette.

#> Time difference of 12.227 secs

R session information.

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#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2023-07-05
#>  pandoc   2.7.3 @ /usr/bin/ (via rmarkdown)
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9 Bibliography

This vignette was generated using BiocStyle (Oleś, 2023) with knitr (Xie, 2023) and rmarkdown (Allaire, Xie, Dervieux et al., 2023) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.23. 2023. URL: https://github.com/rstudio/rmarkdown.

[2] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.

[3] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.28.0. 2023. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.

[4] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2023. URL: https://www.R-project.org/.

[5] K. Rue-Albrecht. iSEEhub: iSEE for the Bioconductor ExperimentHub. R package version 1.2.1. 2023. DOI: 10.18129/B9.bioc.iSEEhub. URL: https://bioconductor.org/packages/iSEEhub.

[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.

[7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2. 2021. URL: https://CRAN.R-project.org/package=sessioninfo.

[8] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.43. 2023. URL: https://yihui.org/knitr/.