Perspectives for better batch effect correction in mass-spectrometry-based proteomics
Open accessAbstract
Mass-spectrometry-based proteomics presents some unique challenges for batch effect correction. Batch effects are technical sources of variation, can confound analysis and usually non-biological in nature. As proteomic analysis involves several stages of data transformation from spectra to protein, the decision on when and what to apply batch correction on is often unclear. Here, we explore several relevant issues pertinent to batch effect correct considerations. The first involves applications of batch effect correction requiring prior knowledge on batch factors and exploring data to uncover new/unknown batch factors. The second considers recent literature that suggests there is no single best batch effect correction algorithm---i.e., instead of a best approach, one may instead ask, what is a suitable approach. The third section considers issues of batch effect detection. And finally, we look at potential developments for proteomic-specific batch effect correction methods and how to do better functional evaluations on batch corrected data.
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Batch effects are common and often much more pronounced in mass spectrometry-based data, even when the same physical instrument is used and the same data type is being generated.
“For example, batch effects are common and often much more pronounced in mass spectrometry-based data (Mertens, 2016; Phua et al., 2022; e.g., Han and Li, 2020), even when the same physical instrument is used and the same data type is being generated.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Phua et al., 2022
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Missing data in multi-omics integration: Recent advances through artificial intelligence
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“For example, batch effects are common and often much more pronounced in mass spectrometry-based data (Mertens, 2016; Phua et al., 2022; e.g., Han and Li, 2020), even when the same physical instrument is used and the same data type is being generated.”
Background
- Authors
- Phua, S.-X., Lim, K.-P., & Goh, W. W.-B.
- Affiliations
- Nanyang Technological University (SG)
- Corresponding author
- Goh, Wilson Wen-Bin
- ORCID
- Lim, Kai-Peng
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2022-01
- Volume
- 20
- Pages
- 4369-4375
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC9411064
- OpenAlex
- W4290861579
- Cited by
- 42
- References
- 43
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 10 · 2023: 6
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DR-NTU (Nanyang Technological University) (submittedVersion, cc-by-nc-nd)
- Funders
- Ministry of Education - Singapore (RG35/20)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- batch effect correction, mass spectrometry-based proteomics, technical variability
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