DESCRIPTION

Package: easier
Title: Estimate Systems Immune Response from RNA-seq data
Version: 1.7.1
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,
    OmnipathR
Suggests: 
    knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment
biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware
VignetteBuilder: knitr
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Config/testthat/edition: 3