Package: easier
Title: Estimate Systems Immune Response from RNA-seq data
Version: 1.19.0
Authors@R: 
    c(
        person(
            given = "Oscar", family = "Lapuente-Santana", role = c("aut", "cre"), 
            email = "o.lapuente.santana@tue.nl", comment = c(ORCID = "0000-0003-1995-8393")
        ),
        person(
            given = "Federico", family = "Marini", role = "aut", 
            email = "marinif@uni-mainz.de", comment = c(ORCID = "0000-0003-3252-7758")
        ),
        person(
            given = "Arsenij", family = "Ustjanzew", role = "aut", 
            email = "arsenij.ustjanzew@uni-mainz.de", comment = c(ORCID = "0000-0002-1014-4521")
        ),
        person(
            given = "Francesca", family = "Finotello", role = "aut", 
            email = "francesca.finotello@i-med.ac.at", comment = c(ORCID = "0000-0003-0712-4658")
        ),
        person(
            given = "Federica", family = "Eduati", role = "aut", 
            email = "f.eduati@tue.nl", comment = c(ORCID = "0000-0002-7822-3867")
        )
    )
Description: 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.
License: MIT + file LICENSE
Depends: R (>= 4.2.0)
Imports: progeny, easierData, dorothea (>= 1.6.0), decoupleR,
        quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr,
        DESeq2, utils, dplyr, tidyr, tibble, matrixStats, rlang,
        BiocParallel, reshape2, rstatix, ggrepel, magrittr, coin
Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment,
        viper
biocViews: GeneExpression, Software, Transcription, SystemsBiology,
        Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics,
        Classification, BiomedicalInformatics, Regression,
        ExperimentHubSoftware
VignetteBuilder: knitr
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Config/testthat/edition: 3
Config/pak/sysreqs: cmake make libicu-dev libpng-dev libssl-dev
        zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:57:29 UTC
RemoteUrl: https://github.com/bioc/easier
RemoteRef: HEAD
RemoteSha: 60fe9abbcc3aa334c8e3db78a2bf0cc9742b3cbd
NeedsCompilation: no
Packaged: 2026-04-30 17:33:50 UTC; root
Author: Oscar Lapuente-Santana [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-1995-8393>),
  Federico Marini [aut] (ORCID: <https://orcid.org/0000-0003-3252-7758>),
  Arsenij Ustjanzew [aut] (ORCID:
    <https://orcid.org/0000-0002-1014-4521>),
  Francesca Finotello [aut] (ORCID:
    <https://orcid.org/0000-0003-0712-4658>),
  Federica Eduati [aut] (ORCID: <https://orcid.org/0000-0002-7822-3867>)
Maintainer: Oscar Lapuente-Santana <o.lapuente.santana@tue.nl>
Built: R 4.6.0; ; 2026-04-30 17:44:24 UTC; windows
