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cff-version: 1.2.0
message: 'To cite package "PRONE" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'PRONE: The PROteomics Normalization Evaluator'
version: 1.1.0
doi: 10.32614/CRAN.package.PRONE
abstract: High-throughput omics data are often affected by systematic biases introduced
  throughout all the steps of a clinical study, from sample collection to quantification.
  Normalization methods aim to adjust for these biases to make the actual biological
  signal more prominent. However, selecting an appropriate normalization method is
  challenging due to the wide range of available approaches. Therefore, a comparative
  evaluation of unnormalized and normalized data is essential in identifying an appropriate
  normalization strategy for a specific data set. This R package provides different
  functions for preprocessing, normalizing, and evaluating different normalization
  approaches. Furthermore, normalization methods can be evaluated on downstream steps,
  such as differential expression analysis and statistical enrichment analysis. Spike-in
  data sets with known ground truth and real-world data sets of biological experiments
  acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can
  be analyzed.
authors:
- family-names: Arend
  given-names: Lis
  email: lis.arend@tum.de
  orcid: https://orcid.org/0000-0001-7990-8385
repository: https://bioc.r-universe.dev
repository-code: https://github.com/lisiarend/PRONE
commit: fd54f013646ea8ba4639ac87a90fda361581104e
url: https://github.com/lisiarend/PRONE
contact:
- family-names: Arend
  given-names: Lis
  email: lis.arend@tum.de
  orcid: https://orcid.org/0000-0001-7990-8385