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<p>See <a href="https://scholar.google.com/citations?user=LUUIw_EAAAAJ&hl=en">Google Scholar page</a> for other contributing publications.<br /></p>
<h4 id="preprint">Preprint</h4>
<ul>
<li><a href="https://www.biorxiv.org/content/10.1101/2024.07.08.602444v1">Unveiling Clonal Cell Fate and Differentiation Dynamics: A Hybrid NeuralODE-Gillespie Approach.</a><br />
Gao M^, Barile M^, Chabra S, Haltalli M, Calderbank M, Chao M, Laurent E#, Gottgens B#, and Huang Y#.<br />
<strong>bioRxiv</strong> 602444 (2024) | <a href="https://github.com/StatBiomed/clonaltrans">Python package</a></li>
<li><a href="https://www.biorxiv.org/content/10.1101/2024.07.05.602174v1">scTail: precise polyadenylation site detection and its alternative usage analysis from reads 1 preserved 3’ scRNA-seq data.</a><br />
Hou Ruiyan and Huang Yuanhua#.<br />
<strong>bioRxiv</strong> 602174 (2024) | <a href="https://github.com/StatBiomed/scTail">Python package</a></li>
<li><a href="https://www.biorxiv.org/content/10.1101/2024.07.04.602109v1">Interpretable variational encoding of genotypes identifies comprehensive clonality and lineages in single cells geometrically.</a><br />
Chung Hoi Man and Huang Yuanhua#.<br />
<strong>bioRxiv</strong> 602109 (2024) | <a href="https://github.com/StatBiomed/SNPmanifold">Python package</a></li>
<li><a href="https://www.biorxiv.org/content/10.1101/2024.02.17.580852v1">Hist2Cell: Deciphering Fine-grained Cellular Architectures from Histology Images.</a><br />
Zhao Weiqin, Liang Zhuo, Huang Xianjie, Huang Yuanhua# and Yu Lequan#.<br />
<strong>bioRxiv</strong> 580852 (2024) | <a href="https://github.com/Weiqin-Zhao/Hist2Cell">Python package</a></li>
</ul>
<h4 id="2025">2025</h4>
<ul>
<li><a href="https://www.nature.com/articles/s41467-024-55419-z">Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures.</a><br />
Cao D.^, Liu Y.^, Cheng, Y.^, Wang J., Zhang B., …, Yeung W.S.B.#, Huang Y.#, & Yao Y.#<br />
<strong>Nature Communications</strong> 16, 137 | <a href="https://github.com/StatBiomed/stemVAE_endometrium">Python package</a></li>
</ul>
<h4 id="2024">2024</h4>
<ul>
<li><a href="https://www.nature.com/articles/s41467-024-51026-0">Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone.</a><br />
Huang Rongting^, Huang Xianjie^, Tong Yin, Yin Helen Y.N., Leung S.Y., Stegle Oliver and Huang Yuanhua#<br />
<strong>Nature Communications</strong> 15, 6684 | <a href="https://github.com/single-cell-genetics/XClone">Python package</a> |
<a href="https://www.biorxiv.org/content/10.1101/2023.04.03.535352v3">bioRxiv</a></li>
<li><a href="https://www.biorxiv.org/content/10.1101/2023.10.12.562026v1">CellContrast: reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.</a><br />
Li Shumin^, Ma Jiajun^, Zhao Tianyi, Jia Yuran, Liu Bo, Luo Ruibang#, Huang Yuanhua#<br />
<strong>Patterns</strong> 5, 101022 | <a href="https://github.com/HKU-BAL/CellContrast">Python package</a> |
<a href="https://www.biorxiv.org/content/10.1101/2023.10.12.562026v1">bioRxiv</a></li>
<li><a href="https://www.cell.com/patterns/fulltext/S2666-3899(24)00154-5">Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization.</a><br />
Qiao Chen and Huang Yuanhua#<br />
<strong>Patterns</strong> 5, 101021 | <a href="https://github.com/qiaochen/tranSpa">Python package</a> |
<a href="https://www.biorxiv.org/content/10.1101/2023.01.20.524992v2">bioRxiv</a></li>
<li><a href="https://www.nature.com/articles/s41467-024-48282-5">Multiplexed bulk and single-cell RNA-seq hybrid enables cost-efficient disease modeling with chimeric organoids.</a><br />
Cheng Chen^, Wang Gang^, Zhu Yuqin, Wu Hangdi, Zhang Li#, Huang Yuanhua# and Zhang Jin#<br />
<strong>Nature Communications</strong> 15, 3946 | <a href="https://github.com/chengarthur/Vireobulk_analysis">Python package</a></li>
<li><a href="https://academic.oup.com/brain/article/147/2/554/7457295">Expression profiling of cerebrospinal fluid identifies dysregulated antiviral mechanisms in multiple sclerosis.</a><br />
Ban M^, Bredikhin D^, <em>Huang Y</em>^, Bonder MJ, Katarzyna K, Oliver AJ, Wilson NK, Coupland P, Hadfield J, Göttgens B, Madissoon E, Stegle O^, and Sawcer S^<br />
<strong>Brain</strong> 147(2): 554-565 | <a href="https://github.com/huangyh09/MSclerosisSrc">Analysis Scripts</a> | <a href="https://github.com/huangyh09/ViralScan">ViralScan</a> | <a href="https://github.com/huangyh09/doubletQC">DoubletQC</a> | <a href="https://github.com/huangyh09/scQTLib">scQTLib</a></li>
</ul>
<h4 id="2023">2023</h4>
<ul>
<li><a href="https://www.nature.com/articles/s41467-023-42636-1">CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5’ scRNA-seq data.</a><br />
Hou Ruiyan, Hon Chung-Chau and Huang Yuanhua#<br />
<strong>Nature Communications</strong> 14, 7240 | <a href="https://github.com/StatBiomed/CamoTSS">Python package</a> |
<a href="https://www.biorxiv.org/content/10.1101/2023.04.17.536840v1">bioRxiv</a></li>
<li><a href="https://www.nature.com/articles/s41467-023-39608-w">SpatialDM: Rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patterns.</a><br />
Li Zhuoxuan, Wang Tianjie, Liu Pengtao# and Huang Yuanhua#<br />
<strong>Nature Communications</strong> 14, 3935 | <a href="https://github.com/StatBiomed/SpatialDM">Python package</a> | <a href="https://www.biorxiv.org/content/10.1101/2022.08.19.504616v3">bioRxiv</a></li>
<li><a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02980-3">DCATS: differential composition analysis for complex single-cell experimental designs.</a><br />
<em>Lin, X.</em>, Chau, C., <em>Huang, Y.</em>#, & Ho, J.W.K.#<br />
<strong>Genome Biology</strong> in print | <a href="https://github.com/holab-hku/DCATS">R package</a> | <a href="https://www.biorxiv.org/content/10.1101/2022.03.21.485232v1">bioRxiv</a></li>
</ul>
<h4 id="2022">2022</h4>
<ul>
<li><a href="https://www.nature.com/articles/s41467-022-34188-7">UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference.</a><br />
Gao Mingze, Qiao Chen and Huang Yuanhua<br />
<strong>Nature Communications</strong> 13, 6586 (2022) | <a href="https://github.com/StatBiomed/UniTVelo">Python package</a> | <a href="https://www.biorxiv.org/content/10.1101/2022.04.27.489808v1">bioRxiv</a></li>
<li><a href="https://doi.org/10.1093/bioinformatics/btac321">Genomic sequences and RNA binding proteins predict RNA splicing kinetics in various single-cell contexts.</a><br />
Hou Ruiyan and Huang Yuanhua<br />
<strong>Bioinformatics</strong>, btac321 (2022) | <a href="https://github.com/StatBiomed/scRNA-efficiency-prediction">Scripts</a> | <a href="https://www.biorxiv.org/content/10.1101/2021.05.02.442314v1">bioRxiv</a></li>
<li><a href="https://www.nature.com/articles/s41467-022-28845-0">MQuad enables clonal substructure discovery using single cell mitochondrial variants.</a><br />
<em>Kwok A.W.C.</em>, <em>Qiao C</em>, <em>Huang R</em>, Sham M.H., Ho J.W.K.#, <em>Huang Y</em>#.<br />
<strong>Nature Communications</strong> 13, 1205 (2022) | <a href="https://github.com/single-cell-genetics/MQuad">Python package</a> | <a href="https://www.biorxiv.org/content/10.1101/2021.03.27.437331v1">bioRxiv</a></li>
</ul>
<h4 id="2021">2021</h4>
<ul>
<li><a href="https://www.pnas.org/content/118/49/e2105859118">Representation learning of RNA velocity reveals robust cell transitions.</a><br />
Qiao Chen and Huang Yuanhua<br />
<strong>Proc. Natl. Acad. Sci. U.S.A.</strong> 118(49), e2105859118 (2021) | <a href="https://github.com/qiaochen/VeloAE">Python package</a> | <a href="https://www.biorxiv.org/content/10.1101/2021.03.19.436127v1">bioRxiv</a></li>
<li><a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02461-5">BRIE2: computational identification of splicing phenotypes from single-cell
transcriptomic experiments.</a><br />
<em>Huang Y</em>, Sanguinetti G.<br />
<strong>Genome Biology</strong> 22, 251 (2021) |
<a href="https://github.com/huangyh09/brie">Python package</a> |
<a href="https://brie.readthedocs.io">Manual</a> |
<a href="https://github.com/huangyh09/brie-tutorials">Tutorials</a></li>
<li><a href="https://www.sciencedirect.com/science/article/abs/pii/S245231002100069X">Uncertainty vs variability: Bayesian methods for analysis of scRNA-seq data.</a><br />
<em>Huang Y</em>, Sanguinetti G.<br />
<strong>Current Opinion in Systems Biology</strong> 100375 (2021) | review paper</li>
<li><a href="https://doi.org/10.1093/bioinformatics/btab358">Cellsnp-lite: an efficient tool for genotyping single cells.</a><br />
Huang Xianjie and Huang Yuanhua<br />
<strong>Bioinformatics</strong>, 37(23), 4569-4571 (2021)
| <a href="https://www.biorxiv.org/content/10.1101/2020.12.31.424913v1">bioRxiv</a>
| <a href="https://cellsnp-lite.readthedocs.io">software</a></li>
</ul>
<h4 id="before-joining-hku">Before joining HKU</h4>
<ul>
<li><a href="https://doi.org/10.1016/j.stem.2020.07.019">Naive pluripotent stem cells exhibit phenotypic variability that is driven by
genetic variation.</a><br />
Ortmann D., Brown S., Czechanski A., Aydin S., Muraro D., <em>Huang Y.</em>,
Tomaz R.A., Osnato A., Canu G., Wesley B.T., Skelly D., Stegle O., …,
Reinholdt L.G., Vallier L.<br />
<strong>Cell Stem Cell</strong>, 2020, 7(3), pp.470-481.</li>
<li><a href="https://www.nature.com/articles/s41592-020-0766-3">Cardelino: computational integration of somatic clonal substructure and
single-cell transcriptomes.</a><br />
McCarthy DJ * , Rostom R * , <em>Huang Y</em> * , Kunz DJ, Danecek P, Bonder MJ, Hagai T,
Lyu R., Wang W, Gaffney DJ, Simons BD, Stegle O, Teichmann SA.
(*: Joint first author)<br />
<strong>Nature Methods</strong> Volume 17 (2020) p.414–421 |
<a href="https://github.com/single-cell-genetics/cardelino">R package</a> |
<a href="https://htmlpreview.github.io/?https://github.com/single-cell-genetics/cardelino/blob/master/inst/doc/vignette-cloneid.html">Usage examples</a></li>
<li><a href="http://dx.doi.org/10.1186/s13059-019-1865-2">Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without
genotype reference.</a><br />
<em>Huang Y</em>, McCarthy DJ, Stegle O.<br />
<strong>Genome Biology</strong> Volume 20 (2019) p273 |
<a href="https://github.com/single-cell-genetics/vireo">Python package</a> |
<a href="https://vireoSNP.readthedocs.io">Manual</a></li>
<li><a href="https://link.springer.com/protocol/10.1007/978-1-4939-9057-3_12">Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.
</a><br />
<em>Huang Y</em>, Sanguinetti G.<br />
<strong>Methods Mol Biol</strong> Volume 1935 (2019) p.175-185</li>
<li><a href="https://doi.org/10.1093/bioinformatics/bty1033">Systematic characterization and prediction of post-translational modification
cross-talk between proteins.</a><br />
Huang R, <em>Huang Y</em>, Guo Y, Ji S, Lu M, Li T.<br />
<strong>Bioinformatics</strong> Volume 35 (2019) p.2626-2633</li>
<li><a href="http://www.genome.org/cgi/doi/10.1101/gr.225615.117">Transcription rate strongly affects splicing fidelity and cotranscriptionality
in budding yeast.</a><br />
Aslanzadeh V, <em>Huang Y</em>, Sanguinetti G, Beggs JD.<br />
<strong>Genome Research</strong> Volume 28 (2018) p.203-213</li>
<li><a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1248-5">BRIE: transcriptome-wide splicing quantification in single cells.
</a><br />
<em>Huang Y</em>, Sanguinetti G.<br />
<strong>Genome Biololgy</strong> Volume 18 (2017) p.123 |
<a href="https://github.com/huangyh09/brie">Python package</a> |
<a href="https://brie.readthedocs.io">Manual</a></li>
<li><a href="https://doi.org/10.1093/bioinformatics/btw364">Statistical modeling of isoform splicing dynamics from RNA-seq time series
data.</a><br />
<em>Huang Y</em>, Sanguinetti G.<br />
<strong>Bioinformatics</strong> Volume 32 (2016) p.2965-2972</li>
<li><a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0848-1">Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU
labeling.</a><br />
Barrass JD, Reid JE, <em>Huang Y</em>, Hector RD, Sanguinetti G, Beggs JD, Granneman S. (*: Joint first author)<br />
<strong>Genome Biology</strong> Volume 16 (2015) p.282</li>
<li><a href="https://doi.org/10.1074/mcp.M114.037994">Systematic characterization and prediction of post-translational modification
cross-talk.</a><br />
<em>Huang Y</em>, Xu B, Zhou X, Li Y, Lu M, Jiang R, Li T.<br />
<strong>Mol Cell Proteomics</strong> Volume 14 (2015) p.761-770</li>
</ul>
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