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Quantification with RNAseq - lab 9, GGG 201b WQ 2024

Run pipeline and install software before running RStudio Server

Allocate compute node

Log into farm with ssh, and use srun to get a compute node:

srun -p high2 --time=3:00:00 --nodes=1 --cpus-per-task=4 --mem=5GB --pty /bin/bash

Update from github

Now, update the repo:

cd ~/2024-ggg-201b-rnaseq/
git pull

::::spoiler Do this if you didn't clone the github directory last time:

Run:

cd ~/
git clone https://github.com/ngs-docs/2024-ggg-201b-rnaseq
cd ~/2024-ggg-201b-rnaseq

::::

Finish running quantification pipeline

Load mamba:

module load mamba
mamba activate quant

and run quantification pipeline:

cd ~/2024-ggg-201b-rnaseq
snakemake -j 4 -p

Install RNAseq notebook environment

Next, install a notebook containing RNAseq software in R:

mamba env create -n rnaseq -f environment.yml

Q: what does this install? Take a look at environment.yml.

(We'll be using DESeq2 for analysis of bulk RNAseq data.)

Start RStudio

Now run:

module load rstudio-server
rstudio-launch

and set up your connection as normal.

::::spoiler If you are running in ondemand:

You'll need to start a new RStudio session using the rnaseq conda environment. :::

Running the RMarkdown Notebook

Using the file browser, go to the 2024-ggg-201b-rnaseq directory in your home directory.

Click on the rnaseq-workflow.Rmd.

Now click on "Knit".

Wait a minute or two, and check out the popup window.

Questions to address!

  • What is RMarkdown?
  • What does this notebook do?
  • What is the output of the quantification pipeline, and how does it get loaded into this notebook?
  • What are the outputs of this notebook??

Statistics questions:

  • what is the difference between pvalue and pAdj, and which should you use?
  • what do we do with the outlier on the MDS plot?
  • why does the MA plot look the way it does? What are the striations, and why is there a funnel?

Relevant: green jelly beans