Author

Sheung Chi Phillip Yam

Chinese University of Hong KongORCID 0000-0002-4380-0919Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:scRNA-seq· 2 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Research Focus:imputation· 1 Approach:matrix factorization· 1 Approach:statistical model· 1 Research Focus:trajectory inference· 1

Frequent Co-Authors

Jinghan HuangiD· 2 shared Nelson L.S. TangiD· 2 shared Anson C.M. Chow· 1 shared KS Leung· 1 shared Minghua DengiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

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5 top collaborators · 8 mutual co-author links

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2 shared papersBioinformatics

Jinghan HuangORCID

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2 papers

Recent·Most cited

BioinformaticsJournal of Translational MedicinePeer-reviewedOct 21, 2025

Compositional data modeling of high-dimensional single cell RNA-seq (CoDA-hd): its advantages over commonly used normalization approaches

Jinghan Huang, Sheung Chi Phillip Yam, KS Leung…·Nelson L.S. Tang

This paper introduces CoDA-hd, a compositional data analysis approach for scRNA-seq, showing its advantages in dimension reduction, clustering, and trajectory inference compared to log-normalization.

1 citation

scRNA-seqstatistical model

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BioinformaticsbioRxivPreprintOct 21, 2024

Low-Rank Full Matrix Factorization for dropout imputation in single cell RNA-seq and benchmarking with imputation algorithms for downstream applications

Jinghan Huang, Anson C.M. Chow, Nelson L.S. Tang, Sheung Chi Phillip Yam

This paper develops an imputation algorithm for single-cell RNA-seq and benchmarks it against other methods across various downstream analyses.

DOI 10.1101/2024.10.21.619343

scRNA-seqmatrix factorization

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