Package Recipe 'bioconductor-systempipeshiny'

recipe bioconductor-systempipeshiny

systemPipeShiny: An Interactive Framework for Workflow Management and Visualization

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/systemPipeShiny.html
License:: GPL (>= 3)
Recipe:: /bioconductor-systempipeshiny/meta.yaml

systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community.

package bioconductor-systempipeshiny¶

Versions:

1.20.0-0, 1.16.0-0, 1.12.0-0, 1.10.0-0, 1.8.0-0, 1.4.0-0, 1.2.0-0, 1.0.0-0

Depends:

on r-assertthat
on r-base >=4.5,<4.6.0a0
on r-bsplus
on r-crayon
on r-dplyr
on r-drawer >=0.2
on r-dt
on r-ggplot2
on r-glue
on r-htmltools
on r-magrittr
on r-openssl
on r-plotly
on r-r6
on r-rlang
on r-rsqlite
on r-rstudioapi
on r-shiny >=1.6.0
on r-shinyace
on r-shinydashboard
on r-shinydashboardplus >=2.0.0
on r-shinyfiles
on r-shinyjqui
on r-shinyjs
on r-shinytoastr
on r-shinywidgets
on r-spscomps >=0.3.3
on r-spsutil >=0.2.2
on r-stringr
on r-styler
on r-tibble
on r-vroom >=1.3.1
on r-yaml

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-systempipeshiny

to add into an existing workspace instead, run:

pixi add bioconductor-systempipeshiny

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-systempipeshiny

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-systempipeshiny

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-systempipeshiny:<tag>

(see bioconductor-systempipeshiny/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-systempipeshiny/README.html)