English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Normalization methods in mass spectrometry-based analytical proteomics: A case study based on renal cell carcinoma datasets

MPS-Authors
/persons/resource/persons78895

Wiśniewski,  Jacek R.       
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Carvalho, L. B., Teigas-Campos, P. A. D., Jorge, S., Protti, M., Mercolini, L., Dhir, R., et al. (2023). Normalization methods in mass spectrometry-based analytical proteomics: A case study based on renal cell carcinoma datasets. Talanta, 266: 124953. doi:10.1016/j.talanta.2023.124953.


Cite as: https://hdl.handle.net/21.11116/0000-000D-AA52-7
Abstract
Normalization is a crucial step in proteomics data analysis as it enables data adjustment and enhances comparability between datasets by minimizing multiple sources of variability, such as sampling, sample handling, storage, treatment, and mass spectrometry measurements. In this study, we investigated different normalization methods, including Z-score normalization, median divide normalization, and quantile normalization, to evaluate their performance using a case study based on renal cell carcinoma datasets. Our results demonstrate that when comparing datasets by pairs, both the Z-score and quantile normalization methods consistently provide better results in terms of the number of proteins identified and quantified as well as in identifying statistically significant up or down-regulated proteins. However, when three or more datasets are compared at the same time the differences are found to be negligible.