Please use this identifier to cite or link to this item:
Title: Are batch effects still relevant in the age of big data?
Authors: Goh, Wilson Wen Bin 
Yong, Chern Han
Wong, Limsoon
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Goh, W. W. B., Yong, C. H. & Wong, L. (2022). Are batch effects still relevant in the age of big data?. Trends in Biotechnology.
Project: MOE2019-T2-1-042
Journal: Trends in Biotechnology
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.
ISSN: 0167-7799
DOI: 10.1016/j.tibtech.2022.02.005
Rights: © 2022 Elsevier Ltd. All rights reserved. This paper was published in Trends in Biotechnology and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20230317
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles
SBS Journal Articles

Files in This Item:
File Description SizeFormat 
  Until 2023-03-17
10.9 MBAdobe PDFUnder embargo until Mar 17, 2023

Page view(s)

Updated on May 20, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.