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Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets

Chawade, Aakash LU ; Alexandersson, Erik and Levander, Fredrik LU orcid (2014) In Journal of Proteome Research 13(6). p.3114-3120
Abstract
High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly... (More)
High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics.org/normalyzer. (Less)
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
normalization, preprocessing, label-free, mass spectrometry, microarray, proteomics, transcriptomics
in
Journal of Proteome Research
volume
13
issue
6
pages
3114 - 3120
publisher
The American Chemical Society (ACS)
external identifiers
  • wos:000337074500036
  • scopus:84902095881
  • pmid:24766612
ISSN
1535-3893
DOI
10.1021/pr401264n
language
English
LU publication?
yes
id
d092a588-5bca-4bd0-a526-83e4eda66f40 (old id 4549205)
date added to LUP
2016-04-01 11:13:17
date last changed
2025-04-04 14:55:43
@article{d092a588-5bca-4bd0-a526-83e4eda66f40,
  abstract     = {{High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics.org/normalyzer.}},
  author       = {{Chawade, Aakash and Alexandersson, Erik and Levander, Fredrik}},
  issn         = {{1535-3893}},
  keywords     = {{normalization; preprocessing; label-free; mass spectrometry; microarray; proteomics; transcriptomics}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{3114--3120}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Journal of Proteome Research}},
  title        = {{Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets}},
  url          = {{http://dx.doi.org/10.1021/pr401264n}},
  doi          = {{10.1021/pr401264n}},
  volume       = {{13}},
  year         = {{2014}},
}