Video courses

Two course series on analysing sequencing data in R, taught at Weill Cornell and recorded. The videos, the scripts and the data files are open to anyone. No account and no registration is needed.

Both courses assume no prior programming experience. They start with opening RStudio and end with the kind of analysis that goes into a figure. The point is not to demonstrate a pipeline but to make you able to run one yourself and to argue about whether the answer it gives is real.

Introduction to R, bulk RNAseq analysis and GEO

Six lessons taking you from a first session in R and RStudio through a complete differential expression analysis. The course uses edgeR on a human dataset, covers functional enrichment and GSEA, and spends time on the part that is usually skipped: pulling public data down from GEO and DEE2, merging counts from different studies and getting the annotation to line up.

Each lesson folder holds the recording along with the scripts and files used in it, so you can follow along rather than watch.

Lesson 1. Basics in R and RStudio
Lesson 2. RNAseq overview and edgeR
Lesson 3. edgeR human example and functional enrichment analysis
Lesson 4. Q&A: GSEA, volcano plots and heatmaps
Lesson 5. DEE2, merging counts and annotation
Lesson 6. Within-sample normalisation and dimensional reduction visualisation

Open the course materials

R for scRNAseq

Four lessons on single-cell RNA-seq analysis in R, picking up roughly where the bulk course leaves off. The folder is organised into recordings, the scripts used in each lesson and the data files needed to run them, with a data folder per lesson.

Single-cell analysis is where it is easiest to produce a confident-looking result that is an artefact of the processing choices. The course spends time on those choices rather than treating them as defaults to accept.

Notes

Work through the lessons in order. Later ones assume the earlier ones, and the scripts build on each other. You will need a recent version of R and RStudio, both free, and the packages each lesson installs at the start.

Anyone is welcome to use these, including for teaching. If you reuse the material in a course of your own, a credit is appreciated. If a video will not play, a script has gone stale against a newer package version, or you are stuck on a step, email dar2042@med.cornell.edu and we will fix it. Knowing where people get stuck is the most useful feedback we get.

For the analysis code behind our papers rather than teaching material, see the Software page.