MIG MixOmics workshop – 23-35 July, 2018

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Andrew Siebel


T: +61 3 8344 0707

Beginner mixOmics workshop


Day 1 & 2: methods and hands-on. The following broad topics will be covered.

A. Key methodologies in mixOmics and their variants:

  • Exploration of one data set and how to estimate missing values
  • Identification of biomarkers to discriminate different treatment groups
  • Integration of two data sets and identification of biomarkers
  • Repeated measurements design
  • Integration of more than two data sets to identify multi omics signatures

B. Review on the graphical outputs implemented in mixOmics

  • Sample plot representation
  • Variable plot representation for data integration
  • Other useful graphical outputs

C. Case studies and applications

Five case studies will be analysed using the methods presented above, with a focus on transcriptomics, proteomics and 16S metagenomics data sets.

Day 3: bring your own data. Participants will be given the opportunity to analyse their own data under the guidance and the advice of the instructors. Participants can also work in a team. Some data sets will also be provided for those unable to bring their own data.

The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of diagnostic or prognostic markers, l1 and l2 penalties in a regression framework. Each methodology will be illustrated on a case study (theory and application will alternate). Note that mixOmics is not limited to biological data only and can be applied to other type of data where integration is required.

Target group The course is intended for data analysts in the fields of bioinformatics, computational biology and applied statistics with some statistical knowledge and a good working knowledge in R. It will be particularly useful to those interested in:

  1. Exploring large data sets.
  2. Selecting features with methods implementing LASSO-based penalisations.
  3. Using graphical techniques to better visualise data.
  4. Understanding and/or applying multivariate projection methodologies to large data sets.

Anticipated learning outcomes After completion of this workshop, participants will be able to

  1. Understand fundamental principles of multivariate projection-based dimension reduction technique.
  2. Perform statistical integration and feature selection using recently developed multivariate methodologies.
  3. Apply those methods to high throughput biological studies, including their own studies.

Enquiries: Andrew Siebel (asiebel@unimelb.edu.au)