Author

Chen Qiao

University of Hong KongORCID 0000-0002-4382-3221Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 3 Approach:statistical model· 3 Research Focus:differential expression· 2 Research Focus:trajectory inference· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Yuanhua HuangiD· 4 shared Aaron Wing Cheung KwokiD· 1 shared Joshua W. K. HoiD· 1 shared Mai Har ShamiD· 1 shared Mingze GaoiD· 1 shared Rongting HuangiD· 1 shared

Activity by Year

2021-2024 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 11 mutual co-author links

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4 shared papersBioinformatics

Yuanhua HuangORCID

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4 papers

Recent·Most cited

Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization

Chen Qiao, Yuanhua Huang

This paper introduces TransImp, a model for imputing missing features in spatial transcriptomics with uncertainty estimation and spatial regularization, improving reliability in downstream analyses.

Patterns · Jul 9, 2024Peer-reviewedBioinformatics

UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference

Mingze Gao, Chen Qiao, Yuanhua Huang

UniTVelo improves RNA velocity modeling by incorporating temporal regularization, enhancing trajectory inference across diverse biological systems.

Nature Communications · Nov 3, 2022Peer-reviewedBioinformatics

MQuad enables clonal substructure discovery using single cell mitochondrial variants

Aaron Wing Cheung Kwok, Chen Qiao, Rongting Huang, Mai Har Sham, Joshua W. K. Ho, Yuanhua Huang

MQuad identifies clonally informative mitochondrial variants from single-cell sequencing data with high sensitivity and specificity, outperforming existing methods.

Nature Communications · Mar 8, 2022Peer-reviewedBioinformatics

Representation learning of RNA velocity reveals robust cell transitions

Chen Qiao, Yuanhua Huang

This paper introduces VeloAE, a method to improve the accuracy and stability of cell transition estimation from RNA velocity data across multiple datasets.

Proceedings of the National Academy of Sciences · Dec 3, 2021Peer-reviewedBioinformatics