Computational and Systems Biology

Project Supervisors	Project
Dr Kim-Anh Lê Cao kimanh.lecao@unimelb.edu.au Dr Jarny Choi jarnyc@unimelb.edu.au Dr Matt Ritchie mritchie@wehi.edu.au	Project: Multivariate computational methods for data integration of single cell assays Project Supervisor: Dr Kim-Anh Lê Cao; Project Co-supervisors: Dr Jarny Choi and Dr Matt Ritchie High-throughput single cell molecular profiling gives our scientific community the unique opportunity to define cell types with distinctive molecular profiles to unprecedented depths. However, identifying novel cell types relies on the ability to combine and integrate different types of independent assays (performed in different laboratories) to obtain generalizable and reproducible results. Our main challenges to address are data heterogeneity and large-scale datasets (many cells and many transcripts). The project will focus on mixOmics data integration methods application and extensions. The analysis will be conducted on data we have generated or are available on stemformatics.org. Our aim is to address data heterogeneity challenges and identify robust gene signatures that characterize the novel cell subtypes. The project is suitable for students with a background in computational statistics / biology with a strong interest in cell biology, analytical and critical thinking.

Project Supervisors

Project

Dr Matt Ritchie

mritchie@wehi.edu.au

Project: Multivariate computational methods for data integration of single cell assays

Project Supervisor: Dr Kim-Anh Lê Cao; Project Co-supervisors: Dr Jarny Choi and Dr Matt Ritchie

High-throughput single cell molecular profiling gives our scientific community the unique opportunity to define cell types with distinctive molecular profiles to unprecedented depths. However, identifying novel cell types relies on the ability to combine and integrate different types of independent assays (performed in different laboratories) to obtain generalizable and reproducible results. Our main challenges to address are data heterogeneity and large-scale datasets (many cells and many transcripts). The project will focus on mixOmics data integration methods application and extensions. The analysis will be conducted on data we have generated or are available on stemformatics.org. Our aim is to address data heterogeneity challenges and identify robust gene signatures that characterize the novel cell subtypes. The project is suitable for students with a background in computational statistics / biology with a strong interest in cell biology, analytical and critical thinking.