The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Detecting significant changes in protein abundance
Open accessAbstract
We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labeled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.
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Metadata
- Authors
- Kammers, K., Cole, R. N., Tiengwe, C., & Ruczinski, I.
- Venue
- EuPA Open Proteomics
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 7
- Pages
- 11-19
- ISSN
- 2212-9685 (ISSN-L)
- OpenAlex
- W2006900120
- Cited by
- 311
- References
- 47
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Kammers, K., Cole, R. N., Tiengwe, C., & Ruczinski, I. (2015). Detecting significant changes in protein abundance. EuPA Open Proteomics. https://doi.org/10.1016/j.euprot.2015.02.002
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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