Author

Edward J. Zhao

University of Washington

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:human· 2 Assay:spatial transcriptomics· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Raphaël GottardoiD· 2 shared Cedric R. UytingcoiD· 1 shared Dave Henderson· 1 shared David RossiD· 1 shared Étienne BechtiD· 1 shared Jamie Guenthoer· 1 shared Jason H. Bielas· 1 shared Joseph BeechemiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersBioinformatics

Raphaël GottardoORCID

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3 papers

Recent·Most cited

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Patrick J. Danaher, Edward J. Zhao, Zhi Gang Yang, David Ross, Mark Gregory, Zach Reitz, Tae Kyoung Kim, Sarah K. Baxter, Shaun W. Jackson, Shanshan He, Dave Henderson, Joseph Beechem

Insitutype types cells in spatial transcriptomics with a likelihood model over every expression value, admits image and neighbourhood priors, and is reported to cluster millions of cells quickly.

bioRxiv · Oct 19, 2022 · PreprintBioinformatics

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Edward J. Zhao, Matthew R. Stone, Xing Ren, Jamie Guenthoer, Kimberly S. Smythe, Thomas Pulliam, Stephen R. Williams, Cedric R. Uytingco, Sarah E. B. Taylor, Paul Nghiem, Jason H. Bielas, Raphaël Gottardo

BayesSpace enhances spatial transcriptomics resolution and clustering, revealing tissue structures undetectable at lower resolutions.

Nature Biotechnology · Jun 3, 2021Peer-reviewedBioinformatics

Reply to \"A discriminative learning approach to differential expression analysis for single-cell RNA-seq\"

Étienne Becht, Edward J. Zhao, Robert A. Amezquita, Raphaël Gottardo

The paper reproduces and extends findings from Ntranos et al., showing that MAST with Sidak p-value aggregation outperforms multivariate logistic regression on experimental scRNA-seq data.

bioRxiv · May 30, 2019 · PreprintBioinformatics