Author

Angela Yu Zhang

University of WashingtonORCID 0000-0002-4953-982XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:metagenomics· 2 Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Organism:human· 1

Frequent Co-Authors

Anna PlantingaiD· 2 shared Hongjiao LiuiD· 2 shared Michael C. WuiD· 2 shared Ni ZhaoiD· 2 shared Wodan LingiD· 2 shared Anju Lulla· 1 shared Anthony A. FodoriD· 1 shared James Robert WhiteiD· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersGenomics

Anna PlantingaORCID

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2 papers

Recent·Most cited

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

Hongjiao Liu, Wodan Ling, Xing Hua, Jee‐Young Moon, Jessica S Williams-Nguyen, Xiang Zhan, Anna Plantinga, Ni Zhao, Angela Yu Zhang, Rob D. Knight, Qibin Qi, Robert David Burk, Robert C. Kaplan, Michael C. Wu

The paper introduces a kernel-based method to identify host genetic variants associated with microbiome beta-diversity, finding immunity-related genes influence gut microbiota composition.

bioRxiv · Oct 15, 2021 · PreprintGenomics

Batch effects removal for microbiome data via conditional quantile regression (ConQuR)

Wodan Ling, Ni Zhao, Anju Lulla, Anna Plantinga, Weijia Fu, Angela Yu Zhang, Hongjiao Liu, Zhigang Li, Jun Chen, Timothy W. Randolph, Wei Li Adeline Koay, James Robert White, Lenore J. Launer, Anthony A. Fodor, Katie A. Meyer, Michael C. Wu

This paper introduces ConQuR, a method to remove batch effects in microbiome data by using conditional quantile regression, demonstrating its effectiveness in preserving signals and improving analysis.

bioRxiv · Sep 23, 2021 · PreprintBioinformatics