UE Biostatistics, bioinformatics, modeling (part II)

Course outline

 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 approaches.

6- to analyze a data set of differential gene expression.

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