Author

Danielle Tsao

University of Victoria

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· 2

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:deconvolution· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Xuekui ZhangiD· 2 shared Don D. SiniD· 1 shared Firoozeh V. GerayeliiD· 1 shared Janice M. LeungiD· 1 shared Kailun BaiiD· 1 shared Li XingiD· 1 shared Min TsaoiD· 1 shared Xiangling JiiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Xuekui ZhangORCID

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2 papers

Recent·Most cited

Real-paired single-cell/bulk RNA-seq benchmark and a practical protocol for accurate cell-type deconvolution in human BAL samples

Yushan Hu, Ziying Liu, Danielle Tsao, Janice M. Leung, Firoozeh V. Gerayeli, Xuan Li, Xiaojian Shao, Don D. Sin, Xuekui Zhang

Systematic benchmarking reveals pseudo-bulk biases and provides practical fixes for accurate RNA deconvolution.

bioRxiv · Jan 14, 2026 · PreprintBioinformatics

scAnnotate: an automated cell type annotation tool for single-cell RNA-sequencing data

Xiangling Ji, Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang

scAnnotate is a cell type annotation tool for scRNA-seq data that uses dropout information through a mixture model and ensemble learning, showing competitive performance across multiple datasets.

Bioinformatics Advances · Jan 1, 2023Peer-reviewedBioinformatics