pathMED

[jordimartorell]

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• Repository: https://github.com/jordimartorell/pathMED

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[bioc-issue-bot]

Hi @jordimartorell

Thanks for submitting your package. We are taking a quick
look at it and you will hear back from us soon.

The DESCRIPTION file for this package is:

Package: pathMED
Type: Package
Title: Scoring Personalized Molecular Portraits
Version: 0.99.0
Authors@R: c(person("Jordi", "Martorell-Marugán", email="[email protected]", role=c("cre", "aut"), comment = c(ORCID = "0000-0002-5186-0735")),
  person("Daniel", "Toro-Domínguez", email="[email protected]", role=c("aut"), comment = c(ORCID = "0000-0001-8440-312X")),
  person("Raúl", "López-Domínguez", email="[email protected]", role=c("aut"), comment = c(ORCID = "0000-0001-8634-117X")),
  person("Iván", "Ellson", email="[email protected]", role=c("aut"), comment = c(ORCID = "0000-0001-6307-3141")))
Description:   PathMED is a collection of tools to facilitate precision medicine studies
  with omics data (e.g. transcriptomics). Among its funcionalities, genesets
  scores for individual samples may be calculated with several methods.
  These scores may be used to train machine learning models and to predict 
  clinical features on new data. For this, several machine learning
  methods are evaluated in order to select the best method based on internal 
  validation and to tune the hyperparameters. Performance metrics and a 
  ready-to-use model to predict the outcomes for new patients are returned.
Depends: R (>= 4.5.0)
Suggests: ada,
        AUCell,
        Biobase,
        BiocGenerics,
        BiocStyle,       
        fgsea (>= 1.15.4),
        gam,
        import,
        kernlab,
        klaR,
        knitr,
        mboost,
        MLeval,
        randomForest,
        ranger,
        rmarkdown,
        RUnit,
        utils,
        xgboost
Imports: 
    BiocParallel,
    caret,
    caretEnsemble,
    decoupleR,
    ggplot2,
    GSVA,
    factoextra,
    FactoMineR,
    magrittr,
    matrixStats,
    methods,
    metrica,
    pbapply,
    reshape2,
    singscore,
    stats,
    stringi,
    dplyr,
VignetteBuilder: knitr
biocViews: Pathways,
 Classification,
 FeatureExtraction,
 Transcriptomics
BugReports: https://github.com/jordimartorell/pathMED/issues
URL: https://github.com/jordimartorell/pathMED
License: GPL-2
Encoding: UTF-8
LazyData: false
RoxygenNote: 7.3.2

[lshep]

Could you please provide an abstract/intro section in your vignette that
provides motivation for inclusion in Bioconductor and when appropriate a review
and comparison to existing Bioconductor packages with similar functionality or
scope.

I currently do not see strong interoperability with existing Bioconductor classes. You mention omics - so class structures like SingleCellExperiments or SpatialExperiment should be utilized. you also create or take as input gene lists so class structures like BiocSet or GeneSets/GeneSetCollections.

[jordimartorell]

Dear @lshep,

Thank you for your comments. We added an Introduction in the vignette. We also added compatibility with SumarizedExperiment, ExpressionSet and GeneSetCollection classes in the functions that use omics data and gene sets. These changes are available in the new version 0.99.1.

Best regards,
Jordi