1 Practical matters

1.1 Course website

https://ivanek.github.io/analysisOfGenomicsDataWithR/

  • Includes handouts and link to supplementary data files
  • Updated after the lectures to exercises solutions
  • Bookmark it!

1.2 Learning goals

  • Understand the basic concepts involved in genomics data analysis (bulk and single-cell mRNA-Seq, ChIP-Seq)
  • Be able to perform some simple analysis workflow using the Bioconductor framework, visualize and interpret the results.
  • Familiarize with public data repositories (GEO, UCSC)

1.3 Lectures

Wednesday, 14h15-16h, Lecture room 104, Biozentrum.

  • Each course will be a mix of lecture and (guided) exercises
  • Bring your laptop
  • No lecture on 13.03.2019 and 01.05.2019
  • Teachers: bioinformaticians at the Department of Biomedicine of the Unibas, and at the FMI.

1.4 Important

  • Asking questions is highly encouraged
  • If something is not working or understood, reach out for help as early as possible
  • Practice, practice and practice again!

1.5 Requirements

A basic knowledge of R and RStudio

  • Understand the usage of basic data types such as factor or data.frame
  • Load, modify and summarize data fitting into a data.frame
  • Visualize the data, e.g., as a scatterplot or boxplot
  • Run and interpret a simple linear model
  • Understand error messages and get help from the documentation

1.6 Resources for catching-up (and for future reference)

If you find today’s exercises too challenging you will have to catch up fast. Here are a few resources:

1.6.1 Online resources

  • Our introductory course in autumn semester: Introduction to R
  • Introduction to R Programming on edX

1.6.2 Books

  • Dalgaard P (2008) Introductory statistics with R. Springer Science & Business Media. SwissBib link
  • The R chapters in: Buffalo V (2015) Bioinformatics data skills. O’Reilly. SwissBib link

1.6.3 R Cheat sheets

Various cheat sheets at the RStudio website, for example:

1.7 Exam

  • 2 ECTS credits
  • Two exam sessions: 10.04.2019 and 29.05.2019
  • To pass: score >= 50% at each of the 2 exams
  • 45 minutes handwritten exam at beginning of these lectures
  • Correction of the exams and discussion will take place just after the exams
  • All material allowed
  • Computer required

2 Installation and setup

  • See the course webpage for the different options.
  • R needs to be up-to-date (version >= 3.5.0).
  • Ask for help if you encounter troubles.

3 RStudio

  • RStudio is an Integrated Development Environment (IDE) for the R programming language.
  • It is one of the most ergonomic option for learning and using R (syntax highlighting, auto-completion, etc)
  • It enables interactive data analysis, reproducible research and result publishing.
  • It can be locally installed (desktop version) or accessed via a web browser (RStudio server).

3.1 The RStudio interface