See Google Scholar page for other contributing publications.

Preprint

• Unveiling Clonal Cell Fate and Differentiation Dynamics: A Hybrid NeuralODE-Gillespie Approach.
  Gao M^, Barile M^, Chabra S, Haltalli M, Calderbank M, Chao M, Laurent E#, Gottgens B#, and Huang Y#.
  bioRxiv 602444 (2024) | Python package
• scTail: precise polyadenylation site detection and its alternative usage analysis from reads 1 preserved 3’ scRNA-seq data.
  Hou Ruiyan and Huang Yuanhua#.
  bioRxiv 602174 (2024) | Python package
• Interpretable variational encoding of genotypes identifies comprehensive clonality and lineages in single cells geometrically.
  Chung Hoi Man and Huang Yuanhua#.
  bioRxiv 602109 (2024) | Python package
• Hist2Cell: Deciphering Fine-grained Cellular Architectures from Histology Images.
  Zhao Weiqin, Liang Zhuo, Huang Xianjie, Huang Yuanhua# and Yu Lequan#.
  bioRxiv 580852 (2024) | Python package

2024

• Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone.
  Huang Rongting^, Huang Xianjie^, Tong Yin, Yin Helen Y.N., Leung S.Y., Stegle Oliver and Huang Yuanhua#
  Nature Communications (in print) | Python package | bioRxiv
• CellContrast: reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.
  Li Shumin^, Ma Jiajun^, Zhao Tianyi, Jia Yuran, Liu Bo, Luo Ruibang#, Huang Yuanhua#
  Patterns 5, 101022 | Python package | bioRxiv
• Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization.
  Qiao Chen and Huang Yuanhua#
  Patterns 5, 101021 | Python package | bioRxiv
• Multiplexed bulk and single-cell RNA-seq hybrid enables cost-efficient disease modeling with chimeric organoids.
  Cheng Chen^, Wang Gang^, Zhu Yuqin, Wu Hangdi, Zhang Li#, Huang Yuanhua# and Zhang Jin#
  Nature Communications 15, 3946 | Python package
• Expression profiling of cerebrospinal fluid identifies dysregulated antiviral mechanisms in multiple sclerosis.
  Ban M^, Bredikhin D^, Huang Y^, Bonder MJ, Katarzyna K, Oliver AJ, Wilson NK, Coupland P, Hadfield J, Göttgens B, Madissoon E, Stegle O^, and Sawcer S^
  Brain 147(2): 554-565 | Analysis Scripts | ViralScan | DoubletQC | scQTLib

2023

• CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5’ scRNA-seq data.
  Hou Ruiyan, Hon Chung-Chau and Huang Yuanhua#
  Nature Communications 14, 7240 | Python package | bioRxiv
• SpatialDM: Rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patterns.
  Li Zhuoxuan, Wang Tianjie, Liu Pengtao# and Huang Yuanhua#
  Nature Communications 14, 3935 | Python package | bioRxiv
• DCATS: differential composition analysis for complex single-cell experimental designs.
  Lin, X., Chau, C., Huang, Y.#, & Ho, J.W.K.#
  Genome Biology in print | R package | bioRxiv

2022

• UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference.
  Gao Mingze, Qiao Chen and Huang Yuanhua
  Nature Communications 13, 6586 (2022) | Python package | bioRxiv
• Genomic sequences and RNA binding proteins predict RNA splicing kinetics in various single-cell contexts.
  Hou Ruiyan and Huang Yuanhua
  Bioinformatics, btac321 (2022) | Scripts | bioRxiv
• MQuad enables clonal substructure discovery using single cell mitochondrial variants.
  Kwok A.W.C., Qiao C, Huang R, Sham M.H., Ho J.W.K.#, Huang Y#.
  Nature Communications 13, 1205 (2022) | Python package | bioRxiv

2021

• Representation learning of RNA velocity reveals robust cell transitions.
  Qiao Chen and Huang Yuanhua
  Proc. Natl. Acad. Sci. U.S.A. 118(49), e2105859118 (2021) | Python package | bioRxiv
• BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments.
  Huang Y, Sanguinetti G.
  Genome Biology 22, 251 (2021) | Python package | Manual | Tutorials
• Uncertainty vs variability: Bayesian methods for analysis of scRNA-seq data.
  Huang Y, Sanguinetti G.
  Current Opinion in Systems Biology 100375 (2021) | review paper
• Cellsnp-lite: an efficient tool for genotyping single cells.
  Huang Xianjie and Huang Yuanhua
  Bioinformatics, 37(23), 4569-4571 (2021) | bioRxiv | software

Before joining HKU

• Naive pluripotent stem cells exhibit phenotypic variability that is driven by genetic variation.
  Ortmann D., Brown S., Czechanski A., Aydin S., Muraro D., Huang Y., Tomaz R.A., Osnato A., Canu G., Wesley B.T., Skelly D., Stegle O., …, Reinholdt L.G., Vallier L.
  Cell Stem Cell, 2020, 7(3), pp.470-481.
• Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes.
  McCarthy DJ * , Rostom R * , Huang Y * , Kunz DJ, Danecek P, Bonder MJ, Hagai T, Lyu R., Wang W, Gaffney DJ, Simons BD, Stegle O, Teichmann SA. (*: Joint first author)
  Nature Methods Volume 17 (2020) p.414–421 | R package | Usage examples
• Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference.
  Huang Y, McCarthy DJ, Stegle O.
  Genome Biology Volume 20 (2019) p273 | Python package | Manual
• Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.
  Huang Y, Sanguinetti G.
  Methods Mol Biol Volume 1935 (2019) p.175-185
• Systematic characterization and prediction of post-translational modification cross-talk between proteins.
  Huang R, Huang Y, Guo Y, Ji S, Lu M, Li T.
  Bioinformatics Volume 35 (2019) p.2626-2633
• Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast.
  Aslanzadeh V, Huang Y, Sanguinetti G, Beggs JD.
  Genome Research Volume 28 (2018) p.203-213
• BRIE: transcriptome-wide splicing quantification in single cells.
  Huang Y, Sanguinetti G.
  Genome Biololgy Volume 18 (2017) p.123 | Python package | Manual
• Statistical modeling of isoform splicing dynamics from RNA-seq time series data.
  Huang Y, Sanguinetti G.
  Bioinformatics Volume 32 (2016) p.2965-2972
• Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling.
  Barrass JD, Reid JE, Huang Y, Hector RD, Sanguinetti G, Beggs JD, Granneman S. (*: Joint first author)
  Genome Biology Volume 16 (2015) p.282
• Systematic characterization and prediction of post-translational modification cross-talk.
  Huang Y, Xu B, Zhou X, Li Y, Lu M, Jiang R, Li T.
  Mol Cell Proteomics Volume 14 (2015) p.761-770