• Bioinformatics
• Machine learning
• Single-cell genomics
• Spatial transcriptomics
• Somatic mutations and evolution

Dr Huang is an assistant professor in the School of Biomedical Sciences and the Department of Statistics and Actuarial Science at the University of Hong Kong (HKU). Prior to joining HKU, he was an EBPOD research fellow in the University of Cambridge and European Bioinformatics Institute (EMBL-EBI). Dr Huang completed his BEng in Automation from Tsinghua University (2009-2013) and PhD in Informatics (Machine learning and computational biology) from the University of Edinburgh (2014-2017).

• Department of Statistics and Actuarial Science, University of Hong Kong

We are a 100% dry lab, developing computational methods to achieve accurate quantifications from noisy and/or sparse sequencing data and discover biological patterns from high dimensional omics data.

While we are broadly interested in machine learning methods for biology and health, we currently focus on single-cell data science. Specifically, we develop machine learning algorithms and statistical models to analyse single-cell data, at different omics levels and cellular conditions. Here are a few major ongoing topics in our lab:

1. modelling RNA processing and its intrinsic dynamics, e.g., with RNA velocity, to decipher gene regulation and cell differentiation trajectory.

2. analysing multi-omics data to understand somatic mutations, cancer evolution and their phenotypic impact.

3. integrating spatial transcriptomics and single-cell data to identify cellular spatial interactions and their changes in time and space.

4. developing statistical or neural network models to reveal biological insights.

We welcome students to join us to work on existing research directions or related new topics. Please email Dr. Huang (yuanhua@hku.hk) directly for project details and opportunities.

1. Kwok, A. W. C., Qiao, C., Huang, R., Sham, M. H., Ho, J. W.#, & Huang, Y.#  “MQuad enables clonal substructure discovery using single cell mitochondrial variants.” Nature communications, 2022, 13(1): 1-10.
2. Hou, R., & Huang, Y.# “Genomic sequences and RNA binding proteins predict RNA splicing efficiency in various single-cell contexts.” Bioinformatics. 2022, btac321.
3. Qiao, C., & Huang, Y.# “Representation learning of RNA velocity reveals robust cell transitions.” Proceedings of the National Academy of Sciences, 2021, 118(49).
4. Huang, X., & Huang, Y.#. “Cellsnp-lite: an efficient tool for genotyping single cells.” Bioinformatics, 2021, 37(23): 4569-4571.
5. Huang, Y.#, & Sanguinetti, G.# “BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments.” Genome biology, 2021, 22(1): 1-15.
6. Huang, Y., & Sanguinetti, G. “Uncertainty versus variability: Bayesian methods for analysis of scRNA-seq data.” Current Opinion in Systems Biology, 2021, 28, 100375.
7. McCarthy D.^†, Rostom R.^†, Huang Y.^†, Kunz D., Danecek P., Bonder M, Hagai T., Lyu R., Wang W., Gaffney D.J., Simons B.D., Stegle O., Teichmann S.A. “Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants." Nature Methods ,2020, 17:414-421. ^†co-first author
8. Huang Y., McCarthy D., Stegle O. “Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference." Genome Biology, 2019, 20(1): 273.
9. Huang Y., and Sanguinetti G. “BRIE: transcriptome-wide splicing quantification in single cells." Genome Biology, 2017, 18(1): 123.
10. Huang Y., and Sanguinetti G. “Statistical modeling of isoform dynamics from RNA-seq time series data." Bioinformatics, 2016, 32(19): 2965-2972.
11. Huang Y., Xu B., Zhou X., Li Y., Lu M., Jiang R., and Li T. “Systematic characterization and prediction of post-translational modification cross-talk." Molecular & Cellular Proteomics, 2015, 14(3): 761-770.

• 2017, Best poster award, High Throughput Sequencing algorithms (HiTSeq) workshop, ISMB/ECCB Conference
• 2017, EBPOD postdoctoral fellowship, University of Cambridge and EMBL-European Bioinformatics Institute
• 2018, Chinese Government Award for Outstanding Self-Financed Students Abroad
• 2019, Travel fellowship, Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB), Switzerland