Batch-effect correction with sample remeasurement in highly confounded case-control studies-Reference-Cited by-同舟云学术

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Batch-effect correction with sample remeasurement in highly confounded case-control studies

Published:2023-08-23 Issue:8 Volume:3 Page:709-719
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

Ye Hanxuan^ORCID,Zhang Xianyang,Wang Chen^ORCID,Goode Ellen L.,Chen Jun^ORCID

Funder

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)

Link

https://www.nature.com/articles/s43588-023-00500-8.pdf

Reference35 articles.

1. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

2. Goh, W. W. B., Wang, W. & Wong, L. Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol. 35, 498–507 (2017).

3. Scherer, A. Batch Effects and Noise in Microarray Experiments: Sources and Solutions (John Wiley & Sons, 2009).

4. Tom, J. A. et al. Identifying and mitigating batch effects in whole genome sequencing data. BMC Bioinform. 18, 351 (2017).

5. Price, E. M. & Robinson, W. P. Adjusting for batch effects in dna methylation microarray data, a lesson learned. Front. Genet. 9, 83 (2018).

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Leveraging remeasured samples in biomedical studies;Nature Computational Science;2023-08-23

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