Research Projects
Machine Learning Methods for Analysing Single-Cell Genomic Data

In recent years, the rapid development of single-cell and spatial sequencing technologies brings unprecedented opportunities to disentangle the heterogeneity in cell populations, including different immune cells, differentiation trajectory or cancer mutations. However, it remains highly challenging to decipher how a biological system functions and the underlying patterns of the data, not only because of high technical noise but also the high dimensions of the gene or other molecular feature space. 

Therefore, this project aims to develop machine learning methods, likely in a form of probabilistic models or deep neural networks, to analyse single-cell genomic data. Specific projects will be determined together between the student and PI. Potential directions include but not limited to:

1. Clonal somatic mutations and integrative analysis. This may consider different types of mutations (CNV, SNV, mtDNA) and reconstruct their evolutionary dynamics.
2. Cell trajectory inference and its underlying regulation. This may leverage our recent development of RNA velocity methods.
3. Statistical modelling of spatial transcriptomic data, for higher resolution analysis. This project is likely to link with the modelling of single-cell data and/or imaging data.

We are generally looking for candidates with a strong quantitative background in (scientific) computing, and interests in biomedical challenges.

Professor YH Huang, School of Biomedical Sciences

Professor 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). He was trained in machine learning and bioinformatics at Tsinghua, Edinburgh, and Cambridge universities and the European Bioinformatics Institute (EMBL-EBI). His lab is supported NSFC Excellent Young Scientist Fund and has strong expertise in statistical machine learning for analysing single-cell genomics and broad biomedical data. He is serving as an Editorial Board member for Genome Biology and an Advisory Board member for Patterns.

Biography
HKU Scholars Hub
Lab Homepage
ORCID
yuanhua@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.