More details on the covered topics

1. Key methodologies in mixOmics and their variants

          A. Exploration of one data set and how to estimate missing values

   B. Identification of biomarkers to discriminate different treatment groups

          C. Integration of two data sets and identification of biomarkers

          D. Repeated measurements design

          E. Introduction to the integration of more than two data sets

2. Review on the graphical outputs implemented in mixOmics

          A. Sample plot representation

          B. Variable plot representation for data integration

          C. Other useful graphical outputs

3. Case studies and applications

The following statistical concepts will be introduced: covariance and correlation, multiple linear regression, classification and prediction, cross-validation, selection of diagnostic or prognostic markers, cross-validation, l₁ and l₂ penalties in a regression framework.

Each methodology will be illustrated on a case study (we will alternate theory and application).

Note that mixOmics is not limited to biological data only and can be applied to other type of data where integration is required.