Author

Dickson M. D. Siu

University of Hong KongORCID 0000-0002-1598-7253Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:imaging· 3 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Kevin K. TsiaiD· 4 shared Joshua W. K. HoiD· 2 shared Michelle C. K. LoiD· 2 shared Shobana V. StasseniD· 2 shared Alan S.L. WongiD· 1 shared Andrian YangiD· 1 shared Bei WangiD· 1 shared Crystal S.M. Kwok· 1 shared

Activity by Year

2020-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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4 shared papersBioinformatics

Kevin K. TsiaORCID

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Author profile →

4 papers

Recent·Most cited

Generalizable Morphological Profiling of Cells by Interpretable Unsupervised Learning

Rashmi Sreeramachandra Murthy, Shobana V. Stassen, Dickson M. D. Siu, Michelle C. K. Lo, Gwinky G. K. Yip, Kevin K. Tsia

MorphoGenie is an unsupervised deep-learning framework for morphological profiling of cells that learns interpretable, generalizable features without manual annotation or labeled data.

Nature Communications · Dec 11, 2025Peer-reviewedBioinformatics

On-The-Fly Live-Cell Intrinsic Morphological Drug and Genetic Screens by Gigapixel-per-second Spinning Arrayed Disk Imaging

Dickson M. D. Siu, Victor M. L. Wong, Bei Wang, Justin S. J. Wong, Alan S.L. Wong, Kevin K. Tsia

This paper introduces SpAD, a high-throughput label-free imaging platform for live-cell drug and genetic screens, enabling rapid, unbiased profiling of cellular responses.

bioRxiv · Oct 21, 2025 · PreprintBioengineering

starmapVR: immersive visualisation of single cell spatial omic data

Andrian Yang, Yao Yu, Xiunan Fang, Jianfu Li, Yongyan Xia, Crystal S.M. Kwok, Michelle C. K. Lo, Dickson M. D. Siu, Kevin K. Tsia, Joshua W. K. Ho

starmapVR provides an immersive visualization tool for single-cell and spatial omic data using VR, supporting 3D and 2.5D views with multiple interaction methods.

bioRxiv · Sep 1, 2020 · PreprintBioinformatics

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

Shobana V. Stassen, Dickson M. D. Siu, Kelvin C. M. Lee, Joshua W. K. Ho, Hayden Kwok‐Hay So, Kevin K. Tsia

PARC is a scalable graph-based clustering algorithm for large single-cell datasets, outperforming existing methods in speed and rare population detection.

Bioinformatics · Jan 16, 2020Peer-reviewedBioinformatics