Project Description

Dr Melissa Davis

Laboratory Head, Division of Bioinformatics
Walter and Eliza Hall Institute for Medical Research

Single sample analysis and the visualisation of molecular phenotypes

Bio-data analysis and visualisation

Thursday 5 July 2018

Dr Melissa Davis is a computational biologist and Laboratory Head in the Bioinformatics Division of the Walter and Eliza Hall Institute of Medical Research. Her background is in genetics and computational cell biology with expertise in the analysis of genome-scale molecular networks and knowledge-based modelling.

Melissa received her PhD at the University of Queensland (UQ) and continued as a postdoc at the Institute for Molecular Bioscience. In 2014, she was awarded a National Breast Cancer Foundation Career Development Fellowship, and took up a position as Senior Research Fellow in Computational Systems Biology at the University of Melbourne in the Systems Biology Laboratory, before moving to the Walter and Eliza Hall Institute for Medical Research as a Laboratory Head in 2016. Melissa specialises in the integration of genomic, transcriptomic, and proteomic data with knowledge-based network models to understand the regulatory logic of mammalian systems.

While many methods for assessing molecular phenotypes in cancer data rely on the differential analysis of large sets of samples, we increasingly are faced with small sample sizes or even single samples from patients in a clinical setting. To facilitate the analysis of molecular phenotypes within such small datasets, we have developed a rank-based gene-set scoring method for the dimensional reduction of transcriptomic data. Using this method, we can score samples against a transcriptional signature that characterises a given molecular phenotype, and then find associations between scores and other omics data (e.g. genomics) and/or clinical outcomes (e.g. survival or sensitivity to drugs). This approach can also be used to partition samples based on molecular phenotypes, discovering candidate sub-groups of samples for further multi-omics comparisons. We demonstrate this approach by studying epithelial-mesenchymal transition (EMT), a program implicated in cancer metastasis and drug resistance, as well as assessing the molecular phenotype of single patient samples against a landscape of samples from global cancer sequencing efforts.

Not available.