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Genetech Lab Course

For Whom?

This repo contains all the material for the computer labs associated with the course BB2255. Students attending the CB204V course should also follow this material.

You will find the all the essential information regarding these labs within this repo, such as guidelines regarding the execution of the labs, contact information, deadlines and brief descriptions of the labs with links to material that might be of help to you.

We encourage and strongly recommend that you in groups of three or two. Please state who you are working with in the report and comment section of your hand in, also make sure EVERYONE of you hand in a copy of the report (identical).

🧬 Introduction 🧬

Main Responsible TA: Solène Frapard

This lecture is an introduction to the hands on part of the course and getting everyone up to speed with working in Posit (RStudio) Cloud.

🧬 Lab 1 | Basic R programming and markdown 🧬

Main Responsible TA: Solène Frapard
Lab Status: Ready 👍
Working file: labs/ex1/main.Rmd

Here you will familiarize yourself with some of the basic syntax and documentation in R. This lab will lay the foundation for the two next labs, where the focus will be more on analyzing data with the help of certain bioinformatic packages. Thus it is imperative that you put effort into learning how to orient yourself in R.

Preparatory work

Before Lab 1 starts make sure that you have Posit (RStudio) Cloud setup, as described on the here.

Hand-in

In addition to changing the author information (see Guidelines), also change the variable GRADE_MODE from FALSE to TRUE before handing in the lab, if your lab knitts without any complications, you have solved all the exercises correctly.

Resources

  • Rmarkdown cheat sheet: LINK
  • Learn X in Y minutes (R): LINK
  • Swirl, Learn R in R: LINK

🧬 Lab 2 | Single Cell RNA-seq analysis 🧬

Main Responsible TA: Pontus Höjer
Lab Status: Ready 👍
Working file: labs/ex3/main.Rmd

Single-cell RNA sequencing (or scRNA-seq) has become one of the most valuable tools in genomics to to answer questions about molecular processes at the cellular level. For example, scRNA-seq has been used to define new celltypes and cell states, study cell to cell interactions, track differentiation processes over time, study responses to drugs and much more. scRNA-seq methods have also become important tools in ongoing research efforts to create atlases of organs in the human body in efforts such as the Human Cell Atlas (HCA).

In this lab we will explore the popular Seurat R package for single-cell genomics data analysis. You will be introduced to some of the most basic steps in a scRNA-seq analysis workflow including quality control, normalization, batch correction, dimensionality reduction, unsupervised clustering and differential expression analysis.

🧬 Lab 3 | Spatial Transcriptomics analysis 🧬

Main Responsible TA: Mengxiao He
Lab Status: Ready 👍
Working file: labs/ex4/main.Rmd

In this lab you will work with Visium data, the spatial gene expression of human breast cancer data! While similar to single cell data in some ways, there are also some important differences. One thing that's extremely attractive with spatial transcriptomics data is that the very design of the method allows us to visualize the gene expression in the physical 2D plane - which is one of the exercises in this lab. You will also apply some of the concepts introduced in the previous labs, in order to make sense of your spatial data.

Preparatory work

For the best experience we recommended that you read up a bit on the following concepts, still these concepts will be briefly explained in the lab:

  • Spatial Transcriptomics (The method presented here)
  • Dimensionality Reduction (Focus on PCA)
  • Clustering
  • Latent Dirichlet Allocation (LDA)
  • Single cell and spatial transcriptomics data integration (The method we are using is presented here)