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Dr HUANG, Yuanhua 黃淵華 (joint appointment with Department of Statistics and Actuarial Science)

Dr HUANG, Yuanhua 黃淵華 (joint appointment with Department of Statistics and Actuarial Science)

  • PhD (U of Edinburgh)
  • BEng (Tsinghua U)
L4-62, Laboratory Block, 21 Sassoon Road, Hong Kong
+852 2855 9730
  • Bioinformatics
  • Single-cell genomics
  • RNA splicing
  • Cancer evolution
  • Statistical modelling
  • Machine learning

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

The Huang Lab focuses on development of statistical machine learning methods and computational algorithms for analysing biomedical data, e.g. single-cell RNA-seq and whole genome sequencing data. We extensively work on Bayesian methods and also have interests in deep learning models. We aim to formulise a variety of biomedical challenges into data science problems and have a few major research topics as follows. Please contact Dr Huang if you are interested in joining his lab.

  • Integrative modelling of somatic mutations: In our very recent work, we developed Cardelino, a Bayesian method to integrate bulk exome-seq and single-cell RNA-seq (scRNA-seq) for inferring the underlying clonal structure of somatic mutations (single nucleotide variation, SNV). This method also allows to assign cells to infered clones and identify the transcriptome variability between clones, e.g., higher proliferation. However, the high sparsity in single-cell data is often a big obstacle. Therefore, we are keen to integrate information from multiple levels to more accurately infer clonal structure and mutation evolution, hence deciphering the impact of somatic mutations on transcriptome phenotypes: 1) integrating multiple assays: scRNA-seq, single-cell DNA-seq, scATAC-seq, bulk exome-seq; 2) integrating multiple mutation types, especially SNV and CNVs. We collaborate with the Stegle lab in Heidelberg, DE and the Leung lab at HKU Med and more.
  • Quantification and dynamic modelling of RNA splicing in single cells: In our earlier work, we developed BRIE (Bayesian Regression for Isoform Estimate), a Bayesian method to quantify splicing isoforms in single cells by leveraging a set of gene level sequence features for learning informative priors, which resolves the challenges caused by high sparsity and noises in scRNA-seq data. We are also actively investigating the dynamics of RNA splicing in single cells and aim to identify relevant splicing events associated with cell level activities, e.g., pseudo-time trajectory. This theme is under close collaboration with the Sanguinetti group in Edinburgh, UK.
  • Analysis of multiple omics data in single cells: We are broadly interested in analysing scRNA-seq data to disentangle the heterogeneity of transcriptome in single cell populations, including both discrete groups, e.g., different immune cell types and continuous states, e.g., differentiation pseudo-time. We are interested in both addressing basic technical challenges, e.g., demultiplex without genotypes (see Vireo paper) and doublets removal, and also gaining biological insights, e.g., searching cell type specific eQTLs across multiple patients. Multiple sclerosis is major case study by closely collaborating with the Sawcer lab at Cambridge, UK.

co-first author

  1. 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 (in press)
  2. 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.
  3. 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.
  4. Huang Y., and Sanguinetti G. “BRIE: transcriptome-wide splicing quantification in single cells." Genome Biology, 2017, 18(1): 123.
  5. Huang Y., and Sanguinetti G. “Statistical modeling of isoform dynamics from RNA-seq time series data." Bioinformatics, 2016, 32(19): 2965-2972.
  6. 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.
  7. 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: December 20, 2019