Non-Award
This course provides training in statistical data analysis for complex biological data using the mixOmics R toolkit. Participants will learn to explore, integrate, and interpret data, understand multivariate methods, and practice using the mixOmics R package through case studies.
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 L�� Cao.
Learn to explore, integrate and interpret dataBe 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 methodsGain 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 dataGain 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 packageEngage 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.
To take this course, you should have some familiarity using the R programming language, and a basic understanding of concepts related to statistics.
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 L�� Cao.
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.
Gain an understanding of key concepts underlying multivariate exploratory and integrative methods, and how they can be applied for data analysis.
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.
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.
The course is divided into three modules, covering:
Throughout the course, you will be given an opportunity to interact with Kim-Anh through weekly live Q&A sessions.