Multivariate computational methods for data integration of single cell assays

Supervisors: Kim-Anh Lê Cao and Jarny Choi

Available for: MSc/PhD and undergraduate research projects.

Location: Melbourne Integrative Genomics and Centre for Stem Cell Systems, University of Melbourne

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

Description: 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 are data heterogeneity and large-scale datasets (many cells and many transcripts). The project will focus on the extension of our projection-based multivariate methods implemented in mixOmics (www.mixOmics.org) to single cell sequencing datasets to address these challenges and identify robust gene signatures that characterize the novel cell subtypes.

Background reading: Regev et al. (2017) The Human Cell Atlas bioRxiv
http://www.biorxiv.org/content/early/2017/05/08/121202

Suitable for: Students with a background in statistics or computer science, and an interest in cell biology.