“The advent of long-read sequencing technologies, combined with single-cell or spatial transcriptomics assays, provided new long-read data, measuring isoform expression at single-cell or spatial resolution. This project aims to address the limitation while applying the new isoform-resolution data for characterising cell identity by developing novel computational methods that integrate both types of isoform expression data.
In this research, variants of factor models will be adapted to develop integrative factor models, which jointly capture the information in two types of isoform data through a common subset of isoforms. Multi-scale methods will be used to incorporate spatial information. When only one of the data types is at isoform resolution, to integrate the two data types, I will model isoform-gene relationships, and impute the missing isoform data using detected cell-identity isoform signatures. The inference for the models will be done by using variational inference.”
The University of Melbourne
Li Fu Zhang, is currently studying Bachelor of Science and marjoring in Statistics at the
University of Melbourne, Australia. With a weighted average mark of 95, he has
demonstrated exceptional academic prowess, earning a spot on the Dean’s Honors List in
both his first and second years, ranking within the top 3% of his cohort. His coursework
spans essential topics such as probability theory, statistical inference, stochastic process,
and data science.
Li Fu’s technical competencies include proficiency in Python, C, R, MATLAB, Microsoft
Word, and Excel. He gained industry experience during an internship as a software engineer
at Hisense Communications from December 2023 to January 2024 and as a machine
learning engineer at Institute of Chinese Academy of Sciences from July 2024 to August
2024 . Beyond academics, he actively contributes to the community as a participating the
VCE summer school program.