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Advanced Statistical Genomics (MAST90126)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Year of offer2019
Subject levelGraduate coursework
Subject codeMAST90126
Semester 2
FeesSubject EFTSL, Level, Discipline & Census Date

The subject will cover statistical analysis of data arising from modern genomics, and their practical application using R and specialist software. RNA-seq, epigenomics and metagenomics assays will be introduced, together with properties of the resulting data, appropriate pre-analyses and advanced statistical methods and algorithms. Methods for biomarker discovery, including supervised learning and multivariate analysis techniques will also be covered, as will statistical models and techniques for phylogenetics.

Intended learning outcomes

On completion of this subject, students should have:

  • Knowledge of available public data resources in genomics and the principles and practice of data access and (for human data) privacy
  • Knowledge of key genomics assays for transcriptomics, epigenomics and metagenomics, the corresponding data analysis challenges and the principle approaches to inference
  • An understanding of core techniques in multivariate statistics (including principal components analysis, partial least squares, factor analysis and multivariate regression analysis) and the ability to apply them to high-dimensional genomics data in a suitable software package
  • An understanding of the principles of biomarker discovery, and the main machine learning/statistical techniques for biomarker discovery and validation
  • Knowledge of the principal statistical/evolutionary models for DNA sequences, and their application in phylogenetics analyses

Generic skills

  • In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. In particular: - computer-based data handling and statistical analysis of large data sets using the R software - ability to read, understand, modify and use computer programs for the manipulation of large data sets - time-management: completing assignments according to deadlines while making judgments about time required for different parts of the assessment

Last updated: 27 August 2019