Package: phenomis
Type: Package
Title: Postprocessing and univariate analysis of omics data
Version: 1.0.0
Date: 2022-10-21
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 = "Alyssa", family = "Imbert",
	   email = "alyssa.imbert@france-bioinformatique.fr",
	   role = "ctb"),
	   person(given = "Pierrick", family = "Roger",
	   email = "pierrick.roger@cea.fr",
	   role = "ctb"),
	   person(given = "Sylvain", family = "Dechaumet",
	   email = "sylvain.dechaumet@cea.fr",
	   role = "ctb"),
	   person(given = "Marie", family = "Tremblay-Franco",
	   email = "marie.tremblay-franco@inrae.fr",
	   role = "ctb"),
	   person(given = "Eric", family = "Venot",
	   email = "eric.venot@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: 7.2.0
git_url: https://git.bioconductor.org/packages/phenomis
git_branch: RELEASE_3_16
git_last_commit: 9c1362c
git_last_commit_date: 2022-11-01
Date/Publication: 2022-11-01
NeedsCompilation: no
Packaged: 2022-11-01 22:28:29 UTC; biocbuild
Author: Etienne A. Thevenot [aut, cre]
    (<https://orcid.org/0000-0003-1019-4577>),
  Natacha Lenuzza [ctb],
  Alyssa Imbert [ctb],
  Pierrick Roger [ctb],
  Sylvain Dechaumet [ctb],
  Marie Tremblay-Franco [ctb],
  Eric Venot [ctb]
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
Built: R 4.2.2; ; 2022-11-06 13:47:34 UTC; unix
