Are batch effects still relevant in the age of big data?

Goh, WWB; Yong, CH; Wong, L

Are batch effects still relevant in the age of big data?

Goh, WWB Yong, CH Wong, L

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Trends Biotechnol, 2022, 40, (9), pp. 1029-1040
Issue Date:: 2022-09

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Field	Value	Language
dc.contributor.author	Goh, WWB
dc.contributor.author	Yong, CH
dc.contributor.author	Wong, L
dc.date.accessioned	2023-03-20T02:50:41Z
dc.date.available	2022-02-18
dc.date.available	2023-03-20T02:50:41Z
dc.date.issued	2022-09
dc.identifier.citation	Trends Biotechnol, 2022, 40, (9), pp. 1029-1040
dc.identifier.issn	0167-7799
dc.identifier.issn	1879-3096
dc.identifier.uri	http://hdl.handle.net/10453/167717
dc.description.abstract	Batch effects (BEs) are technical biases that may confound analysis of high-throughput biotechnological data. BEs are complex and effective mitigation is highly context-dependent. In particular, the advent of high-resolution technologies such as single-cell RNA sequencing presents new challenges. We first cover how BE modeling differs between traditional datasets and the new data landscape. We also discuss new approaches for measuring and mitigating BEs, including whether a BE is significant enough to warrant correction. Even with the advent of machine learning and artificial intelligence, the increased complexity of next-generation biotechnological data means increased complexities in BE management. We forecast that BEs will not only remain relevant in the age of big data but will become even more important.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Trends Biotechnol
dc.relation.isbasedon	10.1016/j.tibtech.2022.02.005
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	06 Biological Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Biotechnology
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Big Data
dc.subject.mesh	Machine Learning
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Machine Learning
dc.subject.mesh	Big Data
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Big Data
dc.subject.mesh	Machine Learning
dc.title	Are batch effects still relevant in the age of big data?
dc.type	Journal Article
utslib.citation.volume	40
utslib.location.activity	England
utslib.for	06 Biological Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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	2023-03-20T02:50:40Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	40
utslib.citation.issue	9

Abstract:

Batch effects (BEs) are technical biases that may confound analysis of high-throughput biotechnological data. BEs are complex and effective mitigation is highly context-dependent. In particular, the advent of high-resolution technologies such as single-cell RNA sequencing presents new challenges. We first cover how BE modeling differs between traditional datasets and the new data landscape. We also discuss new approaches for measuring and mitigating BEs, including whether a BE is significant enough to warrant correction. Even with the advent of machine learning and artificial intelligence, the increased complexity of next-generation biotechnological data means increased complexities in BE management. We forecast that BEs will not only remain relevant in the age of big data but will become even more important.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167717