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  "Title": "Postprocessing and univariate analysis of omics data",
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  "Description": "The 'phenomis' package provides methods to perform\npost-processing (i.e. quality control and normalization) as\nwell as univariate statistical analysis of single and\nmulti-omics data sets. These methods include quality control\nmetrics, signal drift and batch effect correction, intensity\ntransformation, univariate hypothesis testing, but also\nclustering (as well as annotation of metabolomics data). The\ndata are handled in the standard Bioconductor formats (i.e.\nSummarizedExperiment and MultiAssayExperiment for single and\nmulti-omics datasets, respectively; the alternative\nExpressionSet and MultiDataSet formats are also supported for\nconvenience). As a result, all methods can be readily chained\nas workflows. The pipeline can be further enriched by\nmultivariate analysis and feature selection, by using the\n'ropls' and 'biosigner' packages, which support the same\nformats. Data can be conveniently imported from and exported to\ntext files. Although the methods were initially targeted to\nmetabolomics data, most of the methods can be applied to other\ntypes of omics data (e.g., transcriptomics, proteomics).",
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  "Author": "Etienne A. Thevenot [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-1019-4577>),\nNatacha Lenuzza [ctb],\nMarie Tremblay-Franco [ctb],\nAlyssa Imbert [ctb],\nPierrick Roger [ctb],\nEric Venot [ctb],\nSylvain Dechaumet [ctb]",
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      "author": "Etienne A. Thevenot",
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