Workshop: Introduction to statistical analysis of metabolomics data
Metabolomics is a relatively new field in omics, that is becoming more commonplace for the profiling of small molecules to address a variety of scientific questions in medical research, agriculture and environmental studies. This is a beginner-level hands-on workshop, where we will provide an introduction to commonly used supervised and unsupervised statistical methods for the analysis of metabolomics data. Particular emphasis will be placed on liquid chromatography-mass spectrometry based targeted metabolomics data. We will go through routinely used approaches for handling missing values and removing unwanted variation, both of which are essential steps in the statistical analysis. Some familiarity with R software is assumed and the demonstrations will be based on publicly available data.
Don’t forget your laptop!
Dr Alysha De Livera, Biostatistician, the Centre for Epidemiology and Biostatistics, The University of Melbourne
Dr De Livera (PhD, Statistics) has academic background and professional experience in statistics, biostatistics and bioinformatics. She contributes her statistical expertise to a wide range of problems in medicine, epidemiology and biology, while working on practical statistical approaches and software to handle statistical issues that are motivated by these studies. Her recent methodological interests have focused on statistical methods for identifying biomarkers in large-scale omics data, in particular methods for handling unwanted variation and missing values in metabolomics studies. Dr De Livera actively engages in teaching courses in biostatistics, supervising postgraduate students and providing statistical consulting.
Dr Kaushala Jayawardana, postdoctoral bioinformatician, Metabolomics Laboratory, Baker Institute
Kaushala Jayawardana is a postdoctoral bioinformatician in the Metabolomics Laboratory at the Baker Institute. She was awarded a PhD in Statistics from The Sydney University in 2016, where she studied the integration of multiple high-throughput datasets and clinical data. The title of her thesis is Prognostic methods for integrating data from complex diseases. She joined the Baker Institute in June 2016 and has been working on statistical methods for analysing clinical and lipidomic data.