NormalyzerDE : Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis
(2019) In Journal of Proteome Research 18(2). p.732-740- Abstract
Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and... (More)
Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and normalization quality assessment as well as subsequent or independent differential expression analysis using the empirical Bayes Limma approach. When evaluated on two spike-in data sets the RT-segmented approaches outperformed conventional approaches by detecting more peptides (8-36%) without loss of precision. Furthermore, differential expression analysis using the Limma approach consistently increased recall (2-35%) compared to analysis of variance. The combination of RT-normalization and Limma was in one case able to distinguish 108% (2597 vs 1249) more spike-in peptides compared to traditional approaches. NormalyzerDE provides widely usable tools for performing normalization and evaluating the outcome and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at http://quantitativeproteomics.org/normalyzerde.
(Less)
- author
- Willforss, Jakob LU ; Chawade, Aakash LU and Levander, Fredrik LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- data overview, differential expression analysis, limma, normalization, omics data, preprocessing, proteomics data, R-package, singularity
- in
- Journal of Proteome Research
- volume
- 18
- issue
- 2
- pages
- 732 - 740
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- scopus:85055149585
- pmid:30277078
- ISSN
- 1535-3893
- DOI
- 10.1021/acs.jproteome.8b00523
- language
- English
- LU publication?
- yes
- id
- 822243fa-5f0a-4cd3-b319-f03be89fc46d
- date added to LUP
- 2018-11-20 13:30:37
- date last changed
- 2024-04-15 16:38:50
@article{822243fa-5f0a-4cd3-b319-f03be89fc46d,
abstract = {{<p>Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and normalization quality assessment as well as subsequent or independent differential expression analysis using the empirical Bayes Limma approach. When evaluated on two spike-in data sets the RT-segmented approaches outperformed conventional approaches by detecting more peptides (8-36%) without loss of precision. Furthermore, differential expression analysis using the Limma approach consistently increased recall (2-35%) compared to analysis of variance. The combination of RT-normalization and Limma was in one case able to distinguish 108% (2597 vs 1249) more spike-in peptides compared to traditional approaches. NormalyzerDE provides widely usable tools for performing normalization and evaluating the outcome and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at http://quantitativeproteomics.org/normalyzerde.</p>}},
author = {{Willforss, Jakob and Chawade, Aakash and Levander, Fredrik}},
issn = {{1535-3893}},
keywords = {{data overview; differential expression analysis; limma; normalization; omics data; preprocessing; proteomics data; R-package; singularity}},
language = {{eng}},
number = {{2}},
pages = {{732--740}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Journal of Proteome Research}},
title = {{NormalyzerDE : Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis}},
url = {{http://dx.doi.org/10.1021/acs.jproteome.8b00523}},
doi = {{10.1021/acs.jproteome.8b00523}},
volume = {{18}},
year = {{2019}},
}