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cff-version: 1.2.0
title: GetFeatistics
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Gianfranco
    family-names: Frigerio
    email: frigerio.gianfranco@hsr.it
    affiliation: >-
      Center for Omics Sciences (COSR), IRCCS San Raffaele
      Scientific Institute, Milan, Italy
    orcid: 'https://orcid.org/0000-0002-3538-1443'
identifiers:
  - type: doi
    value: 10.5281/zenodo.12143934
repository-code: 'https://github.com/FrigerioGianfranco/GetFeatistics'
abstract: >
  Within the metabolomics community, tools such as MS-Dial
  and R-based algorithms as XCMS are widely implemented to
  convert raw data to feature tables.

  As demonstrated in a previous work, while conducting
  non-targeted metabolomics studies with defined groups of
  subjects assessing variations of molecules present at low
  concentrations (such as metabolites of exogenous
  compounds) the use of separated pooled quality control
  (QC) samples can be a promising strategy to both improve
  the quality of the dataset and preserving potential
  features deriving from low concentrated molecules.
  Moreover, in observational epidemiologic studies,
  controlling for confounding factors is crucial to assess
  the association of metabolite variations with the
  biological question of interest; also, for longitudinal
  studies, linear models with mixed effects are great
  statistical approach.

  To streamline the data elaboration process of metabolomics
  studies, the GetFeatistics R-package has been developed.
  Starting from a table of features, this package
  encompasses useful functions for the data elaboration of
  targeted and non-targeted studies. For non-targeted
  studies, the functions include the assignment to each
  feature of the identification level given previously
  defined cut-offs, the search for the presence of known
  standards, the filtration of the dataset considering
  pooled QC sample, and also considering separated QC
  samples. For targeted studies, it calculates absolute
  concentrations with the option of the use of weighted
  linear regression curves.

  For statistical analyses, after data transformation and
  scaling, besides basic statistics such as ANOVA with or
  without interactions, it performs multiple linear
  regression models on the entire set of features with
  multiple covariates that can be passed as fixed or random
  effects variables. Finally, it generates some data
  visualization of those models such as volcano plots.

  In conclusion, the GetFeatistics is particularly helpful
  for streamlining the metabolomics data elaboration of
  epidemiological studies within the R environment.
keywords:
  - R-package
  - quality controls
  - multivariate statistics
  - linear models with mixed effects
  - epidemiology
license: GPL-3.0