Abstract

In this computer assignment, we will analyze the results of an RNA-seq dataset. This is one type of high-dimensional -omics data that biomedical scientists frequently use. The raw RNA-seq data are sequence reads, but we will use the processed data from an RNA-seq experiment, often referred to as a count matrix. A count matrix contains for every sample (in the columns of the matrix) and every gene or transcript (in the rows of the matrix) the number of sequencing reads representing that gene or transcript (an integer, i.e., 0, 1, 2, 3, ...). The expression level of that gene is derived from the read count by correcting (normalization) for the total number of sequencing reads in a particular sample.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• How to identify differentially expressed genes across multiple experimental conditions?
• What are the biological functions impacted by the differential expression of genes?
• How to visualise high-dimensional data
• How to cluster similar samples and genes?

Learning Objectives

• To learn the principles of the analysis and visualisation of a multidimensional data analysis. We will use RNA-seq data as an example of a multidimensional -omics dataset.