Package: phenomis
Type: Package
Title: Postprocessing and univariate analysis of omics data
Version: 1.13.0
Date: 2024-09-02
Authors@R: c(
	   person(given = "Etienne A.", family = "Thevenot",
	   email = "etienne.thevenot@cea.fr",
	   role = c("aut", "cre"),
	   comment = c(ORCID = "0000-0003-1019-4577")),
	   person(given = "Natacha", family = "Lenuzza",
	   email = "n.lenuzza@gmail.com",
	   role = "ctb"),
	   person(given = "Marie", family = "Tremblay-Franco",
	   email = "marie.tremblay-franco@inrae.fr",
	   role = "ctb"),
	   person(given = "Alyssa", family = "Imbert",
	   email = "alyssa.imbert@gmail.com",
	   role = "ctb"),
	   person(given = "Pierrick", family = "Roger",
	   email = "pierrick.roger@cea.fr",
	   role = "ctb"),
	   person(given = "Eric", family = "Venot",
	   email = "eric.venot@cea.fr",
	   role = "ctb"),
	   person(given = "Sylvain", family = "Dechaumet",
	   email = "sylvain.dechaumet@cea.fr",
	   role = "ctb")
	   )
Description: The 'phenomis' package provides methods to perform
        post-processing (i.e. quality control and normalization) as
        well as univariate statistical analysis of single and
        multi-omics data sets. These methods include quality control
        metrics, signal drift and batch effect correction, intensity
        transformation, univariate hypothesis testing, but also
        clustering (as well as annotation of metabolomics data). The
        data are handled in the standard Bioconductor formats (i.e.
        SummarizedExperiment and MultiAssayExperiment for single and
        multi-omics datasets, respectively; the alternative
        ExpressionSet and MultiDataSet formats are also supported for
        convenience). As a result, all methods can be readily chained
        as workflows. The pipeline can be further enriched by
        multivariate analysis and feature selection, by using the
        'ropls' and 'biosigner' packages, which support the same
        formats. Data can be conveniently imported from and exported to
        text files. Although the methods were initially targeted to
        metabolomics data, most of the methods can be applied to other
        types of omics data (e.g., transcriptomics, proteomics).
biocViews: BatchEffect, Clustering, Coverage, KEGG, MassSpectrometry,
        Metabolomics, Normalization, Proteomics, QualityControl,
        Sequencing, StatisticalMethod, Transcriptomics
Depends: SummarizedExperiment
Imports: Biobase, biodb, biodbChebi, data.table, futile.logger,
        ggplot2, ggrepel, graphics, grDevices, grid, htmlwidgets,
        igraph, limma, methods, MultiAssayExperiment, MultiDataSet,
        PMCMRplus, plotly, ranger, RColorBrewer, ropls, stats, tibble,
        tidyr, utils, VennDiagram
Suggests: BiocGenerics, BiocStyle, biosigner, CLL, knitr, omicade4,
        rmarkdown, testthat
VignetteBuilder: knitr
License: CeCILL
Encoding: UTF-8
LazyLoad: yes
URL: https://doi.org/10.1038/s41597-021-01095-3
RoxygenNote: 7.2.3
Config/pak/sysreqs: libglpk-dev libgmp3-dev make libicu-dev libxml2-dev
        libmpfr-dev libssl-dev zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2025-10-29 15:18:33 UTC
RemoteUrl: https://github.com/bioc/phenomis
RemoteRef: HEAD
RemoteSha: 2244e33ecd2d1ad2d762596b0844d3ae6eeeebea
NeedsCompilation: no
Packaged: 2026-02-20 07:01:07 UTC; root
Author: Etienne A. Thevenot [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-1019-4577>),
  Natacha Lenuzza [ctb],
  Marie Tremblay-Franco [ctb],
  Alyssa Imbert [ctb],
  Pierrick Roger [ctb],
  Eric Venot [ctb],
  Sylvain Dechaumet [ctb]
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
Built: R 4.6.0; ; 2026-02-20 07:03:10 UTC; windows
