Normalization methods in mass spectrometry-based analytical proteomics : A case study based on renal cell carcinoma datasets
Copyright © 2023. Published by Elsevier B.V..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 2023 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:266 |
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| Enthalten in: |
Talanta - 266(2023), Pt 1 vom: 01. Jan., Seite 124953 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Carvalho, Luis B [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 20.09.2023 Date Revised 20.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.talanta.2023.124953 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359909981 |
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| 520 | |a Copyright © 2023. Published by Elsevier B.V. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Mass spectrometry | |
| 650 | 4 | |a Normalization methods | |
| 650 | 4 | |a Proteomics | |
| 650 | 4 | |a Renal carcinoma | |
| 650 | 7 | |a Proteins |2 NLM | |
| 700 | 1 | |a Teigas-Campos, Pedro A D |e verfasserin |4 aut | |
| 700 | 1 | |a Jorge, Susana |e verfasserin |4 aut | |
| 700 | 1 | |a Protti, Michele |e verfasserin |4 aut | |
| 700 | 1 | |a Mercolini, Laura |e verfasserin |4 aut | |
| 700 | 1 | |a Dhir, Rajiv |e verfasserin |4 aut | |
| 700 | 1 | |a Wiśniewski, Jacek R |e verfasserin |4 aut | |
| 700 | 1 | |a Lodeiro, Carlos |e verfasserin |4 aut | |
| 700 | 1 | |a Santos, Hugo M |e verfasserin |4 aut | |
| 700 | 1 | |a Capelo, José L |e verfasserin |4 aut | |
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