Introduction to iSEEindex
Kevin Rue-Albrecht
University of Oxfordkevin.rue-albrecht@imm.ox.ac.uk
18 October 2024
Source:vignettes/iSEEindex.Rmd
iSEEindex.RmdBasics
Install iSEEindex
R is an open-source statistical environment which can be
easily modified to enhance its functionality via packages. iSEEindex
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
iSEEindex
by using the following commands in your R session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("iSEEindex")
## Check that you have a valid Bioconductor installation
BiocManager::valid()Required knowledge
iSEEindex is based on many other packages and in particular those 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.
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
iSEEindex 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.
Citing iSEEindex
We hope that iSEEindex will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("iSEEindex")
#> To cite package 'iSEEindex' in publications use:
#>
#> Rue-Albrecht K, Marini F (2024). _iSEEindex: iSEE extension for a
#> landing page to a custom collection of data sets_. R package version
#> 1.3.3, <https://github.com/iSEE/iSEEindex>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {iSEEindex: iSEE extension for a landing page to a custom collection of data sets},
#> author = {Kevin Rue-Albrecht and Federico Marini},
#> year = {2024},
#> note = {R package version 1.3.3},
#> url = {https://github.com/iSEE/iSEEindex},
#> }Quick start to using iSEEindex
This is a basic example which shows you how to launch an application that lists publicly available data sets hosted on zenodo.org:
library("iSEEindex")
library("BiocFileCache")
bfc <- BiocFileCache(cache = tempdir())
dataset_fun <- function() {
x <- yaml::read_yaml(system.file(package="iSEEindex", "example.yaml"))
x$datasets
}
initial_fun <- function() {
x <- yaml::read_yaml(system.file(package="iSEEindex", "example.yaml"))
x$initial
}
app <- iSEEindex(bfc, dataset_fun, initial_fun)
if (interactive()) {
shiny::runApp(app, port = 1234)
}
Let’s break down this example step by step, to illustrate the functionality and flexibility of iSEEindex.
First, we load the iSEEindex and BiocFileCache packages, to access their functionality.
Next, we use the BiocFileCache::BiocFileCache()
function, to create a persistent on-disk cache of files that the app can
use for adding and retrieving data files between sessions. This is
particularly useful in this example, as we demonstrate the hosting of
data sets and configuration scripts in RDS files and R scripts remotely,
in a zenodo.org
repository.
Next, we define a function – in this example, named
dataset_fun – that returns a list of metadata
for available data sets. In this example, the information is parsed from
an example file distributed in the inst/ directory of the
package. However, users may define a function that fetches that
information in any way they see fit, e.g. authenticating and accessing
data from privately accessible sources. Requirements for that function
are detailed in the help page ?iSEEindex, for the argument
FUN.datasets.
Next, we define another function – in this example, named
initial_fun – that returns a list of metadata
for available initial configurations of the application state, for each
individual data set. In this example, the information is parsed from an
example file distributed in the inst/ directory of the
package. However, similarly to the previous function, users may define a
function that fetches that information in any way they see fit.
Requirements for that function are detailed in the help page
?iSEEindex, for the argument FUN.initial.
Then, we pass the BiocFileCache object and the two
custom functions defined above to the
iSEEindex::iSEEindex() function, to create an iSEE
application that incorporates a landing page offering users the choice
of data sets and initial configurations fetched by the custom
functions.
Finally, we use the shiny::runApp() function to launch
the app.
Reproducibility
The iSEEindex package (Rue-Albrecht and Marini, 2024) was made possible thanks to:
- R (R Core Team, 2024)
- BiocStyle (Oleś, 2024)
- knitr (Xie, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024)
- sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2021)
- testthat (Wickham, 2011)
This package was developed using biocthis.
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("iSEEindex.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("iSEEindex.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-10-18 10:44:46 UTC"Wallclock time spent generating the vignette.
#> Time difference of 7.568 secsR session information.
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────Bibliography
This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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