- 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 - Review on the graphical outputs implemented in mixOmics
A. Sample plot representation
B. Variable plot representation for data integration
C. Other useful graphical outputs - Case studies and applications
Summer School on Multivariate data analysis methods
for biological data using the R package mixOmics (passed)
Prerequisite and requirements
The audience is expected to have a good working knowledge in R (e.g. handling data frames and perform simple calculations). Attendees are requested to bring their own laptops, having installed the software RStudio http://www.rstudio.com/ and the R package mixOmics (instructions provided prior to the training).
More details on the covered topics
The following statistical concepts will be introduced: covariance and correlation, multiple linear regression,
classification and prediction, cross-validation, selection of diagnostic or prognostic markers, crossvalidation,
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.
