recipe bioconductor-easier

Estimate Systems Immune Response from RNA-seq data






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

(downloads) docker_bioconductor-easier



depends bioconductor-biocparallel:


depends bioconductor-decoupler:


depends bioconductor-deseq2:


depends bioconductor-dorothea:


depends bioconductor-easierdata:


depends bioconductor-progeny:


depends bioconductor-quantiseqr:


depends r-base:


depends r-coin:

depends r-dplyr:

depends r-ggplot2:

depends r-ggpubr:

depends r-ggrepel:

depends r-magrittr:

depends r-matrixstats:

depends r-reshape2:

depends r-rlang:

depends r-rocr:

depends r-rstatix:

depends r-tibble:

depends r-tidyr:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-easier

and update with::

   mamba update bioconductor-easier

To create a new environment, run:

mamba create --name myenvname bioconductor-easier

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-easier/tags`_ for valid values for ``<tag>``)

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