Lecture notes. Written from the material of StatOmics Sga21, licensed CC BY-NC-SA 4.0. These are notes, not a transcript: the material has been reorganised and rewritten. This adaptation carries the same licence, and the original is linked above.

11. Course Description

What this covers

This chapter is the course description for Statistical Genomics Analysis (SGA), rather than a lecture on a statistical method. It answers the orienting questions a student needs before the technical chapters that follow: what the course is about, who it is for, what background it assumes, and who teaches it. It assumes nothing beyond general interest in 'omics data analysis.

Scope of the course

High-throughput 'omics studies generate ever larger datasets and, as a consequence, complex data interpretation challenges. The course focuses on the statistical concepts involved in preprocessing, quantification and differential analysis of high-throughput 'omics data. The core material is built around two data types: shotgun proteomics, and (bulk and single-cell) RNA-sequencing.

Experimental design is treated as essential to correct interpretation in any 'omics study. The course covers how to design a statistically efficient experiment, and also how the choice of design feeds back into how the data are modelled — introducing concepts such as blocking.

All of the course's practical work relies on free, open-source tools in R/Bioconductor. The stated aim is to give beginners a solid basis while also giving those already familiar with standard proteomics and next-generation-sequencing workflows a new perspective on them.

Target audience

The course is oriented towards biologists and bioinformaticians with a particular interest in differential analysis for quantitative 'omics data.

Prerequisites

Two kinds of background are expected:

• A basic statistics course, covering data exploration and descriptive statistics, statistical modelling, and inference — specifically linear models, confidence intervals, t-tests, F-tests, ANOVA, and the chi-squared test. Students who need to revisit these basics are pointed to two online courses: an English-language course at gtpb.github.io/PSLS20 and a Dutch-language one at statomics.github.io/statistiekCursusNotas.
• Programming in R, which is preferred but taught from the basics if needed. Two primers are given: R Basics and R Data Exploration, both hosted on Dodona.

Lecturers

The course is taught by:

• Koen Van den Berge
• Lieven Clement

Sources

• Course description page: docs/omics-statistics/statomics/sga21/indexOldLinksMaterialsKoen/01-course-description.md, converted from indexOldLinksMaterialsKoen.Rmd (statOmics/SGA21 repository, commit 0ad787d4cc2bb2f4636440840a8a923cf6c09839), licensed CC BY-NC-SA 4.0.
• No slides, transcript or exercises were supplied for this chapter; the course description is the entirety of the source material. External links referenced by the course description (the PSLS20 and statistiekCursusNotas prerequisite courses, and the Dodona R primers) are named but their content is not part of this chapter.

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