How missing value imputation is confounded with batch effects and what you can do about it.

Goh, WWB; Hui, HWH; Wong, L

How missing value imputation is confounded with batch effects and what you can do about it.

Goh, WWB Hui, HWH Wong, L

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Drug Discov Today, 2023, 28, (9), pp. 103661
Issue Date:: 2023-09

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Field	Value	Language
dc.contributor.author	Goh, WWB
dc.contributor.author	Hui, HWH
dc.contributor.author	Wong, L
dc.date.accessioned	2024-01-16T03:59:50Z
dc.date.available	2023-06-05
dc.date.available	2024-01-16T03:59:50Z
dc.date.issued	2023-09
dc.identifier.citation	Drug Discov Today, 2023, 28, (9), pp. 103661
dc.identifier.issn	1359-6446
dc.identifier.issn	1878-5832
dc.identifier.uri	http://hdl.handle.net/10453/174567
dc.description.abstract	In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Drug Discov Today
dc.relation.isbasedon	10.1016/j.drudis.2023.103661
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0601 Biochemistry and Cell Biology, 1115 Pharmacology and Pharmaceutical Sciences
dc.subject.classification	Medicinal & Biomolecular Chemistry
dc.subject.classification	3214 Pharmacology and pharmaceutical sciences
dc.subject.mesh	Electronic Data Processing
dc.subject.mesh	Electronic Data Processing
dc.subject.mesh	Electronic Data Processing
dc.title	How missing value imputation is confounded with batch effects and what you can do about it.
dc.type	Journal Article
utslib.citation.volume	28
utslib.location.activity	England
utslib.for	0601 Biochemistry and Cell Biology
utslib.for	1115 Pharmacology and Pharmaceutical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-16T03:59:49Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	28
utslib.citation.issue	9

Abstract:

In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.

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http://hdl.handle.net/10453/174567