Dr HUANG, Yuanhua 黃淵華 (joint appointment with Department of Statistics and Actuarial Science)
- PhD (U of Edinburgh)
- BEng (Tsinghua U)
- Assistant Professor
- Single-cell data science
- Machine learning
- RNA splicing
- Somatic mutations
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
Our lab focuses on development of statistical machine learning methods for analysing single-cell genomic data. Single-cell sequencing, by probing thousands to millions of individual cells at DNA, RNA and other molecular levels, allows to investigate the heterogeneity in cell populations, for example subclones in tumour samples and cell differentiation trajectory. We are particularly interested in 1) integrating multi-omics data to understand the cancer mutations, its evolution and phenotypic impact, and 2) modelling of RNA splicing and its intrinsic dynamics as RNA velocity in single cells. We often model such unknown variables in biological systems via probabilistic graphical models and leverage efficient Bayesian inference algorithms to handle large scale data sets.
We welcome students to join us to work on existing research directions or related new topics. Please email Dr. Huang (firstname.lastname@example.org) directly for project details and opportunities.
- 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
- 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.
- Aslanzadeh V., Huang Y., Sanguinetti G., and Beggs J. “Transcription rate strongly affects splicing fidelity and co-transcriptionality in budding yeast." Genome Research, 2018, 28(2): 203-213.
- Huang Y., and Sanguinetti G. “BRIE: transcriptome-wide splicing quantification in single cells." Genome Biology, 2017, 18(1): 123.
- Huang Y., and Sanguinetti G. “Statistical modeling of isoform dynamics from RNA-seq time series data." Bioinformatics, 2016, 32(19): 2965-2972.
- Barrass D.†, Reid J.†, Huang Y.†, Hector R., Sanguinetti G., Granneman S., and Beggs J. “Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling." Genome Biology, 2015, 16(1): 282.
- 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
Last update: July 3, 2020