mixOmics R Essentials for Biological Data Integration
Gain essential tools and knowledge to analyse complex biological data
What you will learn
Gain contemporary skills and knowledge.
Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. To gain a holistic understanding of biological systems, layers of molecular information – or the omics, including transcriptomics, proteomics, metabolomics, metagenomics – must be statistically integrated.
This short course provides the necessary training in statistical data analysis for complex biological data using the renowned integrative analysis R toolkit, package mixOmics.
This course will be useful to researchers at all levels who work with high-throughput omics data, and are seeking the skills to obtain new and deeper insights into biological mechanisms and biomedical problems being faced.
The course is developed and taught by leading researcher, Kim-Anh Lê Cao.
Learn to explore, integrate and interpret data
Be trained in data exploration, integration and interpretation in order to analyse complex biological data. Learn to evaluate the appropriateness of different data integration methods for a given biological question, and interpret the outputs of each method.
Understand key concepts in multivariate methods
Gain an understanding of key concepts underlying multivariate exploratory and integrative methods, and how they can be applied for data analysis.
Learn essential methods for working with large biological data
Gain an overview of statistical and dimension reduction methods for high-throughput biological data. This will help you develop the ability to mine and integrate these large data sets.
Practice using the mixOmics R package
Engage with detailed case studies and an array of methods and hands-on applications with the mixOmics R package. You’ll have an opportunity to select and apply the relevant method to a biological data set, including your own data, honing your critical thinking skills, analytical skills using R, and ability to mine large data sets in practice.
Who you will learn from
Learn from skilled academics and professional experts who will share invaluable knowledge you can use in your job.
Professor Kim-Anh Lê Cao
Professor in Statistical Genomics, School of Mathematics and Statistics
With a background in mathematical engineering, Kim-Anh has had a dynamic career, including as a biostatistician consultant at QFAB Bioinformatics and as a research group leader at the biomedical University of Queensland Diamantina Institute. She is currently Professor at the University of Melbourne, where her research focuses on the development computational and statistical methods for biological data. She leads the mixOmics team with contributors from France, Australia and Canada.
Fees
For Research Higher Degree students enrolled in a University:
$495 AUD (incl. GST)
For Staff and members from Universities & Not-for-profit organisations:
$825 (incl. GST)
Dates
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Course details
How can we help?
If you have a question about our professional development options or how to apply, please contact student support.