High-throughput data analysis with R

The main objective of this course to teach R statistical environment to be applied to high-throughput biological data. After this course, you will be able to use R for analyzing diverse data types from high-throughput biological experiments with a strong focus on different transcriptomics methods. Half of the course is dedicated to the analysis of data from next-generation sequencing experiments. All the topics will introduce the theoretical aspects of the introduced methodologies, and after that, assignments and activities will provide opportunities to explore the practical ways of performing the analyses.

You will learn how to use R and different Bioconductor packages to analyse data from microarrays, NGS, and flow cytometry experiments. Research topics ranging from differential gene expression via transcription factor binding to cellular markers are explored and used to demonstrate how data analysis works in different situations. 

Course length: 6 weeks

Suggested background knowledge: essentials of molecular biology, essentials of evolution, usage of command line, knowledge about biological databases, working knowledge of R (see our Biological data analysis with R course for this)

Course modules

    Week 1 - Flow cytometry: counting and sorting stained cells

    Week 2 - Next-generation sequencing: introduction and genomic applications

    Week 3 - Quantitative transcriptomics: qRT-PCR

    Week 4 - Advanced transcriptomics: gene expression microarrays

    Week 5 - Next-generation sequencing in transcriptomics: RNA-seq experiments

    Week 6 - Analysis of transcription factor binding

Special requirements: You will have to use the R software for the practical parts of this course. R is a free software programming language and a software environment for statistical computing and graphics. You have to be able to install this free software on your computer. To have a working knowledge of using R and Bioconductor packages, it is strongly recommended to study our Biological data analysis with R course or some equivalent material.