Package Recipe 'bioconductor-easier'

recipe bioconductor-easier

Estimate Systems Immune Response from RNA-seq data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/easier.html
License:: MIT + file LICENSE
Recipe:: /bioconductor-easier/meta.yaml

This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy.

package bioconductor-easier¶

Versions:

1.16.0-0, 1.12.0-0, 1.8.0-0, 1.6.3-0, 1.4.0-0, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-decoupler >=2.16.0,<2.17.0
on bioconductor-deseq2 >=1.50.0,<1.51.0
on bioconductor-dorothea >=1.22.0,<1.23.0
on bioconductor-easierdata >=1.16.0,<1.17.0
on bioconductor-progeny >=1.32.0,<1.33.0
on bioconductor-quantiseqr >=1.18.0,<1.19.0
on r-base >=4.5,<4.6.0a0
on r-coin
on r-dplyr
on r-ggplot2
on r-ggpubr
on r-ggrepel
on r-magrittr
on r-matrixstats
on r-reshape2
on r-rlang
on r-rocr
on r-rstatix
on r-tibble
on r-tidyr

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-easier

to add into an existing workspace instead, run:

pixi add bioconductor-easier

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-easier

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-easier

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-easier:<tag>

(see bioconductor-easier/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-easier/README.html)