8 Differential gene expression exercise

Instructor: Leo

8.1 Recap

So far we know how to:

choose a study from recount3
download data for a study with recount3::create_rse()
explore the data interactively with iSEE
expand Sequence Read Archive (SRA) attributes
- sometimes we need to clean them up a bit before we can use them
use edgeR::calcNormFactors() to reduce composition bias
- We didn’t show it explicitly this time
build a differential gene expression model with model.matrix()
explore and interpret the model with ExploreModelMatrix
use limma::voom() and related functions to compute the differential gene expression statistics
extract the DEG statistics with limma::topTable(sort.by = "none")

among several other plots and tools we learned along the way.

Alternatively to recount3, we have learned about the RangedSummarizedExperiment objects produced by SPEAQeasy and in particular the one we are using on the smokingMouse project.

You might have your own data already. Maybe you have it as an AnnData python object. If so, you can convert it to R with zellkonverter.

8.2 Exercise

Exercise option 1: This will be an open ended exercise. Think of it as time to practice what we’ve learnt using data from recount3 or another subset of the smokingMouse dataset. You could also choose to re-run code from earlier parts of the course and ask clarifying questions. You could also use this time to adapt some of the code we’ve covered to use it with your own dataset.

If you prefer a more structured exercise:

Exercise option 2:

Choose two recount3 studies that can be used to study similar research questions. For example, two studies with brain samples across age.
Download and process each dataset independently, up to the point where you have differential expression t-statistics for both. Skip most of the exploratory data analyses steps as for the purpose of this exercise, we are most interested in the DEG t-statistics.

If you don’t want to choose another recount3 study, you could use the smokingMouse data and subset it once to the pups in nicotine arm of the study and a second time for the pups in the smoking arm of the study.
Or you could use the GTEx brain data from recount3, subset it to the prefrontal cortex (PFC), and compute age related expression changes. That would be in addition to SRA study SRP045638 as was showcased in the 2023 version of this course.

recount3::create_rse_manual(
    project = "BRAIN",
    project_home = "data_sources/gtex",
    organism = "human",
    annotation = "gencode_v26",
    type = "gene"
)

Make a scatterplot of the t-statistics between the two datasets to assess correlation / concordance. You might want to use GGally::ggpairs() for this https://ggobi.github.io/ggally/reference/ggpairs.html. Or ggpubr::ggscatter() https://rpkgs.datanovia.com/ggpubr/reference/ggscatter.html.

For example, between the GTEx PFC data and the data from SRA study SRP045638 provided by recount3.
Or between the nicotine-exposed pups and the smoking-exposed pups in smokingMouse.
Or using the two recount3 studies you chose.

Are there any DEGs FDR < 5% in both datasets? Or FDR < 5% in dataset 1 that have a p-value < 5% in the other one?

You could choose to make a concordance at the top plot like at http://leekgroup.github.io/recount-analyses/example_de/recount_SRP019936.html, though you will likely need more time to complete this.