A$500 – A$1,500

Beginner mixOmics microbiome Workshop 15-17 April 2019

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Alan Gilbert Building (Theatre 4, Level 1)

161 Barry Street

Carlton, VIC 3053

Australia

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Refunds up to 30 days before event

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Description

Beginner workshop for microbiome analysis

Complex microbial networks have a central role in the provision and regulation of ecosystems. Multiple microbial biotechnology applications are contributing to global efforts to achieve sustainability - through purification of wastewater, waste valorisation, bioenergy production, or to understand the role of microbiome in human disease and healthy states.

Statistical analysis of microbiome data is challenging due to the inherent characteristics of the data, such as high sparsity and compositional structure. Our workshop will introduce major concepts including multivariate dimension methods developed in
mixOmics. Our methods make no distributional assumptions, are highly flexible for unsupervised (exploratory), supervised (classification) and integration analyses.

This hands-on course will cover basic processing and inherent characteristics of microbiome data (compositionality, batch effects), various analytical frameworks ranging from data exploration, selection of microbial markers, integration with other omics datasets and introduction to time-course analysis. Each methodology introduced in the workshop will be illustrated on real biological studies. The third day is ‘bring your own data’ day where you can reinforce your learnings on your own data!

Pre-requisite: a good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) to fully benefit from the course.

Instructors: Dr Kim-Anh Lê Cao and Dr Olivier Chapleur


Outline

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

A. Key methodologies in mixOmics and their variants:

  • Basic processing of count data (scaling, how to handle compositional data)

  • Exploration of one data set and how to estimate missing values

  • Identification of a microbial signature to discriminate different treatment groups

  • Integration of two data sets and identification of microbial markers

  • Introduction to repeated measurements or longitudinal studies analysis

  • How to deal with batch effects

  • Integration of more than two data sets to identify multi omics signatures (if applicable)

  • Integration of independent but related studies (if applicable)

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

Several microbiome studies will be analysed using the methods presented above.


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 three 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 microbial markers, penalised regressions. Each methodology will be illustrated on a case study (theory and application will alternate).

Target group The course is intended for microbiologists working 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 microbiome data sets.

  2. Selecting microbial 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 microbiome studies, including their own studies.


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Date and Time

Location

Alan Gilbert Building (Theatre 4, Level 1)

161 Barry Street

Carlton, VIC 3053

Australia

View Map

Refund Policy

Refunds up to 30 days before event

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