tfcb_2021

Lecture 17: Introduction to bulk RNA-seq analysis

We will apply all our learnings to undertake analysis of RNA-seq data. In this lecture, we will use R/Bioconductor to perform bulk RNA-seq analysis and in the next two lectures we will Python to perform single-cell RNA-seq analysis.

This lecture will begin with a introduction to RNA-seq and the steps in RNA-seq analysis. We will then undertake a hands-on excercise using R and Bioconductor tools to analyze RNA-seq data generated using Glioblastoma Multiforme patient samples.

Learning Objectives

Bulk RNA-seq data QC and generating count matrices from alignment files
Differential gene expression analysis
Visualization of bulk RNA-seq data

Class materials

The lecture slides are available here
The Notebook which will be using for this lecture is available at Lecture17.ipynb. If you have difficulty performing a git pull to obtain the materials for this class, it is likely because you have a conflict between Lecture17.ipynb) and the version in the public GitHub repo. You can resolve this by making a copy of that markdown (naming it something different, like my_Lecture17.ipynb)) and then discarding changes to the original markdown file.
The bam files for generation of count matrices are available here and the Glioblastoma patient data is available here. Please download (and unzip the files) to the data folder.

Class preparation

Environment set up

We will create a new conda environment for the RNA-seq lectures

# Create new conda environment
conda create -n tfcb2021_rna r r-tidyverse r-irkernel bioconductor-plyranges bioconductor-genomicfeatures bioconductor-rtracklayer bioconductor-ggbio bioconductor-deseq2 bioconductor-enhancedvolcano   bioconductor-goseq r-pheatmap r-rcolorbrewer  bioconductor-org.hs.eg.db -c conda-forge -c bioconda

# Activate conda environment
conda activate tfcb2021_rna

Follow the instructions here to setup the new conda environment in VSCode. Repeated below for convenience.

Open the Terminal window in VSCode (Terminal > New Terminal) and activate the tfcb2021_rna environment you previously created with conda activate tfcb2021_rna.
Type which R into Terminal to identify where the R interpreter is located in the environment (ex. /usr/local/anaconda3/envs/tfcb2021_rna/bin/R).
Now switch back to the base environment with conda activate base.
Type the location identified in Step 2 (ex. /usr/local/anaconda3/envs/tfcb2021_rna/bin/R). This should start the R interpreter.
In the R interpreter, type IRkernel::installspec() after the >. This should install kernelspec in the right location.

Data files

The bam files for generation of count matrices are available here and the Glioblastoma patient data is available here. Please download (and unzip the files) to the data folder.