Multivariate projection methodologies for big data- Postponed

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This event has been postponed to 2016 with a date tba. 

SSAI is pleased to present the following workshop:

Multivariate projection methodologies for big data 

About the workshop

The objective of this tutorial is to introduce the fundamental concepts behind projection-based approaches and illustrate their application on some exemplar studies using the R package mixOmics.

Multivariate projection approaches are useful exploratory tools to get a first understanding of large and complex data sets. These approaches are extremely efficient on large data sets, and can also answer complex questions. Such approaches include Principal Component Analysis (PCA, Joliffe 2002) and other variants, Partial Least Squares regression (PLS, Wold 2001), PLS-Discriminant Analysis, Canonical Correlation Analysis (CCA, Hotelling 1936). These approaches enable dimension reduction by projecting the data into a smaller subspace. Recent developments proposed the so-called `sparse’ approaches, which include Lasso penalisations to allow variable selection (Tibshirani 2001).

PCA is the oldest and most popular multivariate technique but often, little is known about how this approach is solved and what are the limitations. More sophisticated approaches like PLS and CCA have recently been extended to deal with the large dimension (sparse PLS, or regularized CCA) and were proven to bring biologically meaningful results in many studies. Contrary to PCA, PLS and CCA enable the integration of two types of data sets.

Since 2009, we have implemented several multivariate approaches and their sparse variants in the R package mixOmics to be used by the statistical and bioinformatics community. Full tutorials are given on our website: http://perso.math.univ-toulouse.fr/mixomics/

 

About the presenter

Dr Kim-Anh Lê Cao was awarded her PhD in 2008 at Université de Toulouse, France. She started her postdoc in late 2008 at IMB, and has been working as a research-only academic at QFAB until 2013.

She is now working in the University of Queensland Diamantina Institute where she continues developing statistical approaches for the analysis and the integration of large biological data sets. Her research interests are multidisciplinary, focusing on mathematical statistics characterization of molecular biological systems, and developing sound statistical frameworks to apply to addressing new biological questions arising from these frontier molecular technologies. Her main research focus is on variable selection for biological data (`omics’ data) coming from different functional levels by the means of dimension reduction approaches.

Since working at UQ, she has been teaching Statistics as a ResTeach recipient to undergraduate students, and recently for the UQ Bioinformatics Master’s program.

 

Course Outline 

In this course, we will focus on the application of these approaches to medium and high throughput biological data using PCA, CCA, PLS, PLS-DA and the variants that the mixOmics team and collaborators have developed.

Course Timetable
(including morning tea, lunchtime and afternoon tea breaks)

Registrations open at 8:00am on the first day.

Morning and tea breaks after ‘interpreting outputs’, lunchtime between Sections 1 and 2 for each day.

 

Day 1

Principal Component Analysis

Concepts
Interpreting outputs
Application on some real biological data sets, including the applicants own data
Canonical Correlation Analysis

Concepts and limitations
Application in some case studies, including the applicants own data if applicable.
Day 2

Partial Least Squares regression

PLS
sparse PLS
Application in some case studies, including the applicants own data if applicable.
PLS – Discriminant Analysis

PLS
sparse PLS
Application in some case studies, including the applicants own data if applicable.
The course will be delivered in a form of a lectures followed by practicals in R. Course material will be available.

 

Target Audience

Postgraduate students, postdocs and researchers with good statistical knowledge and fluent in R programming, in need to

-explore large data sets

-use graphical techniques

-understand and/or apply multivariate projection methodologies to large data sets.

Learning Objectives

The objective of this short course is to introduce the fundamental concepts of multivariate and projection-based approaches to be directly applicable (and potentially on the spot, time provided) on the applicants own data.
Delegates are required to bring their own laptops equipped with R (http://www.rstudio.com/)

Course Fees

Early Bird

Non-members  $650
Members   $400
Non-member students $300
Member Students  $250

Regular Fee

Non-members  $700
Members   $450
Non-member students $350
Member Students  $300

Travel Expenses

Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. However, please contact the SSAI Office before making any travel arrangements to confirm that the workshop will go ahead, because the SSAI will not be held responsible for any travel or accommodation expenses incurred due to a workshop cancellation.

Cancellation Policy 

Cancellations received prior to 16 July 2015 will be refunded in full. After this time no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to [email protected]
Travel Expenses

Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. However, please contact the SSAI Office before making any travel arrangements to confirm that the workshop will go ahead, because the SSAI will not be held responsible for any travel or accommodation expenses incurred due to a workshop cancellation.

 

This event has been postponed to 2016 with a date tba. 

 

 

 

 

 

 

 

 

 

 

 

 

 

Multivariate projection methodologies for big data- Postponed
When: 23/07/2015 - 24/07/2015
Time: 9:00 am - 5:00 pm
Cost: from $250.00
Location: Queensland University of Technology,
2 George Street,
Brisbane,
QLD 4000
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