Perspectives for better batch effect correction in mass-spectrometry-based proteomics
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,...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/168633 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-168633 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1686332023-06-12T15:31:56Z Perspectives for better batch effect correction in mass-spectrometry-based proteomics Phua, Ser-Xian Lim, Kai-Peng Goh, Wilson Wen Bin School of Biological Sciences Lee Kong Chian School of Medicine (LKCMedicine) Center for Biomedical Informatics Science::Biological sciences Proteomics Batch Effects 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. Ministry of Education (MOE) Published version WWBG acknowledge support from an MOE AcRF Tier 1 award (RG35/20). 2023-06-12T08:27:15Z 2023-06-12T08:27:15Z 2022 Journal Article Phua, S., Lim, K. & Goh, W. W. B. (2022). Perspectives for better batch effect correction in mass-spectrometry-based proteomics. Computational and Structural Biotechnology Journal, 20, 4369-4375. https://dx.doi.org/10.1016/j.csbj.2022.08.022 2001-0370 https://hdl.handle.net/10356/168633 10.1016/j.csbj.2022.08.022 36051874 2-s2.0-85136030033 20 4369 4375 en RG35/20 Computational and Structural Biotechnology Journal © 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Science::Biological sciences Proteomics Batch Effects |
spellingShingle |
Science::Biological sciences Proteomics Batch Effects Phua, Ser-Xian Lim, Kai-Peng Goh, Wilson Wen Bin Perspectives for better batch effect correction in mass-spectrometry-based proteomics |
description |
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. |
author2 |
School of Biological Sciences |
author_facet |
School of Biological Sciences Phua, Ser-Xian Lim, Kai-Peng Goh, Wilson Wen Bin |
format |
Article |
author |
Phua, Ser-Xian Lim, Kai-Peng Goh, Wilson Wen Bin |
author_sort |
Phua, Ser-Xian |
title |
Perspectives for better batch effect correction in mass-spectrometry-based proteomics |
title_short |
Perspectives for better batch effect correction in mass-spectrometry-based proteomics |
title_full |
Perspectives for better batch effect correction in mass-spectrometry-based proteomics |
title_fullStr |
Perspectives for better batch effect correction in mass-spectrometry-based proteomics |
title_full_unstemmed |
Perspectives for better batch effect correction in mass-spectrometry-based proteomics |
title_sort |
perspectives for better batch effect correction in mass-spectrometry-based proteomics |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/168633 |
_version_ |
1772828415910477824 |