Biostatistic, bioinformatics and modeling

Overview of the principal techniques of statistical data treatment, with an emphasis on practical skills and the use of the statistical software R.

At the end of the course, the students should be able to analyze a ""omic"" dataset. More precisely, they should be able:
1- to load, explore and summarize graphically a dataset;
2- to compute confidence interval estimates for proportions, means and variances;
3- to formulate hypotheses, compute tests statistics, interpret p-values and make practical decisions for the
standard parametric and non-parametric tests;
4- to adjust simple and multiple linear models, analyses of variance (anovas), logistic regression, Cox
model;
5- to select genes that explain a response variable by applying multiple testing approach;
6- to analyze a data set of differential gene expression.