Biostatistics

Modern high-throughput assays generate large amounts of data that must be handled and processed appropriately to extract meaningful biological knowledge and generate testable hypotheses. Proficiency in data handling and processing, and the ability to unravel and highlight complex relationships in biological data using adequate tools and methods constitute a crucial skill for the modern molecular biologist. Methods for the analysis, interpretation and integration of such complex large scale (BIG) biological data, require a good background in statistics and bioinformatics for their application and the verification of the final results.
The aims of this course are (i) in the Biostatistics segment to make the students familiar with the statistical theory and terminology, so to understand the power and pitfalls of statistical analysis, with special emphasis on the planning of experiments for the analysis of large scale biological data, (ii) in the molecular segment to provide a primer on methods for the analysis of gene expression (RNA-Seq) data and the interpretation of the final results. Both segments will be carried on in the frame of the R programming language and software environment, seen as an effective tool for large data analysis.

After following this course, the students are expected to:

1. Know the syntax of the R programming language, and how to import data into the R environment.
2. correctly analyse experimental data in the field of Life Sciences
3. interpret experimental data
4 perform basic statistical tests
5 Correctly analyse, interpret and visualize the results of dirrerential gene expression analyses, based on RNA sequencing data