Bioinformatics analysis of spatial data in congenital heart diseases

Primary supervisor

David Dowe

Co-supervisors

  • Dr Hieu Nim (Murdoch Children's Research Institute)
  • Prof Mirana Ramialison (Murdoch Children's Research Institute)

Congenital heart disease affects 1 in 100 babies. Spatial gene expression patterns are critical to understand how the heart develops and what underlying genetic patterns are behind heart malformation. High-throughput spatial temporal data have been recently generated with spatial transcriptomics technologies. Capitalising on these rich datasets, we aim to build a custom analysis workflow in which the cells are profiled with precise spatial gene expression information. The student will provide fundamental contribution to of this project, by:

(1) analysing the spatial and single-cell RNA-seq datasets;  and

(2) cross-validating the pattern in independent datasets.

URLs/references

https://ramialison-lab.github.io/index.html

 

Alan M Turing  paper on morphogenesis

 

C S Wallace and D L Dowe (1999a), "Minimum Message Length and Kolmogorov complexity", Computer J

Required knowledge

Skills Focus

proficiency in one programming language (e.g. Matlab, R, Python), machine learning / deep learning / data science skills, basic understanding of cell and development biology, simulation, data visualisation, bulk / single-cell / spatial RNA-sequencing analysis.

 

Skills Terms

programming; R; Python; Statistical methods; machine learning; differential expression; bulk RNAseq; scRNAseq; Linux/unix; spatial transcriptomics