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.Rbuildignore 100644 0 kb 100644 5 kb
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codecov.yml 100644 0 kb
# `iSEEu` <!-- badges: start --> [![GitHub issues](]( [![GitHub pulls](]( [![Lifecycle: stable](]( [![R-CMD-check-bioc](]( [![Codecov test coverage](]( <!-- badges: end --> The `iSEEu` package contains material and code that extends the `iSEE` package ( We welcome contributions from the community, see below for more instructions. For example, during the Developer Day at the European Bioconductor 2019 conference (`#EuroBioc2019`, at the UCLouvain, in Brussels, Belgium), we proposed a hackathon-like session, and we focused on the design of "modes", i.e. preconfigured sets of panels and linked content to be used as starting setup when launching `iSEE`. ## Installation `iSEEu` can be easily installed from Bioconductor using `BiocManager::install()`: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("iSEEu") ``` Optionally, if you want to install the development version from GitHub, you can use: ```r BiocManager::install("iSEE/iSEEu", dependencies = TRUE) # or alternatively... remotes::install_github("iSEE/iSEEu", 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. ## Expanding the `iSEE` universe with `iSEEu` - install `iSEE` first - the development version is recommended. ```r BiocManager::install("iSEE", version = "devel") # or remotes::install_github("iSEE/iSEE") ``` - fork the `iSEEu` repo ( and clone it locally. ```bash git clone[your_github_username]/iSEEu.git ``` - make the desired changes in the files - start from the `R` folder, then document via `roxygen2` - and push to your fork. - once your contribution (function, panel, mode) is done, consider adding some information in the package. Some examples might be a screenshot of the mode in action (to be placed in the folder `inst/modes_img`), or a well-documented example use case (maybe an entry in the `vignettes` folder). Also add yourself as a contributor (`ctb`) to the DESCRIPTION file. - make a pull request to the original repo - the GitHub site offers a practical framework to do so, enabling comments, code reviews, and other goodies. - more on documenting and code guidelines: - if possible, please consider adding an example in the dedicated Roxygen preamble to show how to run each function - if possible, consider adding one or more unit tests - we use the `testthat` framework ## Some more info #### Where do I look for constants within `iSEE`? Many of the "global" variables that are used in several places in `iSEE` are defined in the [constants.R]( script in `iSEE`. We suggest to use these constants rather than hardcoding (e.g.) column names in the panel specification data frames, to protect against potential future changes of the precise column names. To access a constant, use `iSEE:::.constantName`. #### Is there any example I can check out to understand how things are supposed to work? There are several modes already defined in the `R/` directory. #### Are there any style guides I am supposed to follow? Yes. Mainly guided by common sense of "never changing a working system", please stick to the conventions we have been adopting for developing the existing codebase. A few simple style options: - keep the indentation as it is in the initial functions already available. - if writing text (e.g. vignette), please use one sentence per line - this makes `git diff` operations easier to check. - in code, use a degree of balance. - for names, try to keep some consistency with what already is existing. We use camelCase for modes and some other functions, and prepend most unexported elements with a dot. #### What if I need a custom panel type? In addition to the eight standard panel types, custom panels are easily accommodated within `iSEE` applications. For a guide, see the corresponding [vignette]( For examples, see [this repo]( #### Are there other examples on how to use `iSEE` for exploring other datasets/data types? Yes, you can have a look at the examples in, where we tried to put together fully worked vignettes to re-analyze publicly available datasets, e.g. also trying to replicate some key visualizations of the original publications. #### Where can I find a comprehensive introduction to `iSEE`? The `iSEE` package contains several vignettes detailing the main functionality. You can also take a look at this [workshop]( A compiled version from the Bioc2019 conference (based on Bioconductor release 3.10) is available [here]( ## Code of Conduct Please note that the iSEEu project is released with a [Contributor Code of Conduct]( By contributing to this project, you agree to abide by its terms.