{
  "_id": "6a298144732311cd875b2fca",
  "Package": "metamorphr",
  "Title": "Tidy and Streamlined Metabolomics Data Workflows",
  "Version": "0.4.1.9000",
  "Authors@R": "person(\"Yannik\", \"Schermer\", , \"yannik.schermer@chem.rptu.de\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0009-0002-5201-057X\"))",
  "Description": "Facilitate tasks typically encountered during metabolomics\ndata analysis including data import, filtering, missing value\nimputation (Stacklies et al. (2007)\n<doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012)\n<doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017)\n<doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001)\n<doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad\net al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle\net al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020)\n<doi:10.1038/s41598-020-72664-6>) transformation, centering and\nscaling (Van Den Berg et al. (2006)\n<doi:10.1186/1471-2164-7-142>) as well as statistical tests and\nplotting. 'metamorphr' introduces a tidy (Wickham et al. (2019)\n<doi:10.21105/joss.01686>) format for metabolomics data and is\ndesigned to make it easier to build elaborate analysis\nworkflows and to integrate them with 'tidyverse' packages\nincluding 'dplyr' and 'ggplot2'.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "LazyData": "true",
  "Config/testthat/edition": "3",
  "URL": "https://github.com/yasche/metamorphr,\nhttps://yasche.github.io/metamorphr/",
  "BugReports": "https://github.com/yasche/metamorphr/issues",
  "VignetteBuilder": "knitr",
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  "Repository": "https://yasche.r-universe.dev",
  "Date/Publication": "2026-06-10 13:13:53 UTC",
  "RemoteUrl": "https://github.com/yasche/metamorphr",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
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  "Author": "Yannik Schermer [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0002-5201-057X>)",
  "Maintainer": "Yannik Schermer <yannik.schermer@chem.rptu.de>",
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    "name": "Yannik Schermer",
    "description": "🎓 PhD student. 🧫 in vitro toxicology, 🧪 metabolomics and 📊 reproducible data science."
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    "extra/citation.json",
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    "%>%",
    "calc_km",
    "calc_kmd",
    "calc_neutral_loss",
    "calc_nominal_km",
    "collapse_max",
    "collapse_mean",
    "collapse_median",
    "collapse_min",
    "convert_from_matrix",
    "convert_from_wide",
    "create_metadata_skeleton",
    "filter_blank",
    "filter_cv",
    "filter_global_mv",
    "filter_grouped_mv",
    "filter_msn",
    "filter_mz",
    "filter_neutral_loss",
    "formula_to_mass",
    "impute_bpca",
    "impute_global_lowest",
    "impute_knn",
    "impute_lls",
    "impute_lod",
    "impute_mean",
    "impute_median",
    "impute_min",
    "impute_nipals",
    "impute_ppca",
    "impute_rf",
    "impute_svd",
    "impute_user_value",
    "join_metadata",
    "msn_calc_nl",
    "msn_scale",
    "normalize_cyclic_loess",
    "normalize_factor",
    "normalize_median",
    "normalize_pqn",
    "normalize_quantile_all",
    "normalize_quantile_batch",
    "normalize_quantile_group",
    "normalize_quantile_smooth",
    "normalize_ref",
    "normalize_sum",
    "plot_pca",
    "plot_volcano",
    "read_featuretable",
    "read_featuretable_mzmine",
    "read_mgf",
    "remove_empty_cols",
    "scale_auto",
    "scale_center",
    "scale_level",
    "scale_pareto",
    "scale_range",
    "scale_vast",
    "scale_vast_grouped",
    "summary_featuretable",
    "transform_log",
    "transform_power"
  ],
  "_datasets": [
    {
      "name": "atoms",
      "title": "A tibble containing the NIST standard atomic weights",
      "object": "atoms",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "Number",
        "Element",
        "Isotope",
        "Symbol",
        "Weight",
        "Composition",
        "Standard_Weight"
      ],
      "rows": 442,
      "table": true,
      "tojson": true
    },
    {
      "name": "toy_metaboscape",
      "title": "A small toy data set created from a feature table in MetaboScape style",
      "object": "toy_metaboscape",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "UID",
        "Feature",
        "Sample",
        "Intensity",
        "RT",
        "m/z",
        "Name",
        "Formula"
      ],
      "rows": 110,
      "table": true,
      "tojson": true
    },
    {
      "name": "toy_metaboscape_metadata",
      "title": "Sample metadata for the fictional dataset 'toy_metaboscape'",
      "object": "toy_metaboscape_metadata",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "Sample",
        "Group",
        "Replicate",
        "Batch",
        "Factor"
      ],
      "rows": 11,
      "table": true,
      "tojson": true
    },
    {
      "name": "toy_mgf",
      "title": "A small toy data set containing MSn spectra",
      "object": "toy_mgf",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "VARIABLEONE",
        "VARIABLETWO",
        "VARIABLETHREE",
        "PEPMASS",
        "MSn"
      ],
      "rows": 3,
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "atoms",
      "title": "A tibble containing the NIST standard atomic weights",
      "topics": [
        "atoms"
      ]
    },
    {
      "page": "calc_km",
      "title": "Calculate the Kendrick mass",
      "topics": [
        "calc_km"
      ]
    },
    {
      "page": "calc_kmd",
      "title": "Calculate the Kendrick mass defect (KMD)",
      "topics": [
        "calc_kmd"
      ]
    },
    {
      "page": "calc_neutral_loss",
      "title": "Calculate neutral losses from precursor ion mass and fragment ion masses",
      "topics": [
        "calc_neutral_loss"
      ]
    },
    {
      "page": "calc_nominal_km",
      "title": "Calculate the nominal Kendrick mass",
      "topics": [
        "calc_nominal_km"
      ]
    },
    {
      "page": "collapse_max",
      "title": "Collapse intensities of technical replicates by calculating their maximum",
      "topics": [
        "collapse_max"
      ]
    },
    {
      "page": "collapse_mean",
      "title": "Collapse intensities of technical replicates by calculating their mean",
      "topics": [
        "collapse_mean"
      ]
    },
    {
      "page": "collapse_median",
      "title": "Collapse intensities of technical replicates by calculating their median",
      "topics": [
        "collapse_median"
      ]
    },
    {
      "page": "collapse_min",
      "title": "Collapse intensities of technical replicates by calculating their minimum",
      "topics": [
        "collapse_min"
      ]
    },
    {
      "page": "convert_from_matrix",
      "title": "Convert a wide matrix to a tidy tibble",
      "topics": [
        "convert_from_matrix"
      ]
    },
    {
      "page": "convert_from_wide",
      "title": "Convert a wide feature table to a tidy tibble",
      "topics": [
        "convert_from_wide"
      ]
    },
    {
      "page": "create_metadata_skeleton",
      "title": "Create a blank metadata skeleton",
      "topics": [
        "create_metadata_skeleton"
      ]
    },
    {
      "page": "filter_blank",
      "title": "Filter Features based on their occurrence in blank samples",
      "topics": [
        "filter_blank"
      ]
    },
    {
      "page": "filter_cv",
      "title": "Filter Features based on their coefficient of variation",
      "topics": [
        "filter_cv"
      ]
    },
    {
      "page": "filter_global_mv",
      "title": "Filter Features based on the absolute number or fraction of samples it was found in",
      "topics": [
        "filter_global_mv"
      ]
    },
    {
      "page": "filter_grouped_mv",
      "title": "Group-based feature filtering",
      "topics": [
        "filter_grouped_mv"
      ]
    },
    {
      "page": "filter_msn",
      "title": "Filter Features based on occurrence of fragment ions",
      "topics": [
        "filter_msn"
      ]
    },
    {
      "page": "filter_mz",
      "title": "Filter Features based on their mass-to-charge ratios",
      "topics": [
        "filter_mz"
      ]
    },
    {
      "page": "filter_neutral_loss",
      "title": "Filter Features based on occurrence of neutral losses",
      "topics": [
        "filter_neutral_loss"
      ]
    },
    {
      "page": "formula_to_mass",
      "title": "Calculate the monoisotopic mass from a given formula",
      "topics": [
        "formula_to_mass"
      ]
    },
    {
      "page": "impute_bpca",
      "title": "Impute missing values using Bayesian PCA",
      "topics": [
        "impute_bpca"
      ]
    },
    {
      "page": "impute_global_lowest",
      "title": "Impute missing values by replacing them with the lowest observed intensity (global)",
      "topics": [
        "impute_global_lowest"
      ]
    },
    {
      "page": "impute_knn",
      "title": "Impute missing values using nearest neighbor averaging",
      "topics": [
        "impute_knn"
      ]
    },
    {
      "page": "impute_lls",
      "title": "Impute missing values using Local Least Squares (LLS)",
      "topics": [
        "impute_lls"
      ]
    },
    {
      "page": "impute_lod",
      "title": "Impute missing values by replacing them with the Feature 'Limit of Detection'",
      "topics": [
        "impute_lod"
      ]
    },
    {
      "page": "impute_mean",
      "title": "Impute missing values by replacing them with the Feature mean",
      "topics": [
        "impute_mean"
      ]
    },
    {
      "page": "impute_median",
      "title": "Impute missing values by replacing them with the Feature median",
      "topics": [
        "impute_median"
      ]
    },
    {
      "page": "impute_min",
      "title": "Impute missing values by replacing them with the Feature minimum",
      "topics": [
        "impute_min"
      ]
    },
    {
      "page": "impute_nipals",
      "title": "Impute missing values using NIPALS PCA",
      "topics": [
        "impute_nipals"
      ]
    },
    {
      "page": "impute_ppca",
      "title": "Impute missing values using Probabilistic PCA",
      "topics": [
        "impute_ppca"
      ]
    },
    {
      "page": "impute_rf",
      "title": "Impute missing values using random forest",
      "topics": [
        "impute_rf"
      ]
    },
    {
      "page": "impute_svd",
      "title": "Impute missing values using Singular Value Decomposition (SVD)",
      "topics": [
        "impute_svd"
      ]
    },
    {
      "page": "impute_user_value",
      "title": "Impute missing values by replacing them with a user-provided value",
      "topics": [
        "impute_user_value"
      ]
    },
    {
      "page": "join_metadata",
      "title": "Join a featuretable and sample metadata",
      "topics": [
        "join_metadata"
      ]
    },
    {
      "page": "msn_calc_nl",
      "title": "Calculate neutral losses from precursor ion mass and fragment ion masses",
      "topics": [
        "msn_calc_nl"
      ]
    },
    {
      "page": "msn_scale",
      "title": "Scale intensities in MSn spectra to the highest value within each spectrum",
      "topics": [
        "msn_scale"
      ]
    },
    {
      "page": "normalize_cyclic_loess",
      "title": "Normalize intensities across samples using cyclic LOESS normalization",
      "topics": [
        "normalize_cyclic_loess"
      ]
    },
    {
      "page": "normalize_factor",
      "title": "Normalize intensities across samples using a normalization factor",
      "topics": [
        "normalize_factor"
      ]
    },
    {
      "page": "normalize_median",
      "title": "Normalize intensities across samples by dividing by the sample median",
      "topics": [
        "normalize_median"
      ]
    },
    {
      "page": "normalize_pqn",
      "title": "Normalize intensities across samples using a Probabilistic Quotient Normalization (PQN)",
      "topics": [
        "normalize_pqn"
      ]
    },
    {
      "page": "normalize_quantile_all",
      "title": "Normalize intensities across samples using standard Quantile Normalization",
      "topics": [
        "normalize_quantile_all"
      ]
    },
    {
      "page": "normalize_quantile_batch",
      "title": "Normalize intensities across samples using grouped Quantile Normalization with multiple batches",
      "topics": [
        "normalize_quantile_batch"
      ]
    },
    {
      "page": "normalize_quantile_group",
      "title": "Normalize intensities across samples using grouped Quantile Normalization",
      "topics": [
        "normalize_quantile_group"
      ]
    },
    {
      "page": "normalize_quantile_smooth",
      "title": "Normalize intensities across samples using smooth Quantile Normalization (qsmooth)",
      "topics": [
        "normalize_quantile_smooth"
      ]
    },
    {
      "page": "normalize_ref",
      "title": "Normalize intensities across samples using a reference feature",
      "topics": [
        "normalize_ref"
      ]
    },
    {
      "page": "normalize_sum",
      "title": "Normalize intensities across samples by dividing by the sample sum",
      "topics": [
        "normalize_sum"
      ]
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