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DESeq2 dataset

For today's lesson we will be working with RNA-seq data from a recent publication in Nature Communications by Shan et al. (2021). Before spring break, our final end product was the creation of counts files using HTSeq-count.

So what does the count data actually represent? The count data used for differential expression analysis represents the number of sequence reads that originated from a particular gene. The higher the number of counts, the more reads associated with that gene, and the assumption that there was a higher level of expression of that gene in the sample.

deseq_counts

Our overall goal is to identify a list of genes that are statistically distinct between the groups being compared.

The publication

T cell identity is established during thymic development, but how it is maintained in the periphery remains unknown.

tcell

The authors in this paper discover that by ablating Tcf1 and Lef1 transcription factors in mature CD8+ T cells this induces the expression of genes from non-T cell lineages. We will focus on analyzing the Tcf7 dataset. Specifically, Tcf7 fl/fl mice were crossed with hCD2-Cre mice to create mature CD8+ T cells that lacked Tcf7. The protein coded by Tcf7 is Tcf1.

graphical_abstract

The figures we will create and interpret

  • PCA & correlation heatmap to show clusters of samples based on their similarity
  • Box plots to show expression (normalized counts) of select genes
  • Heatmap to show the expression of the significantly up- and down-regulated genes in the Tcf7 knock-out (KO) versus the wildtype (WT)
  • Volcano plots (essentially a scatter plot) to show the significance (adjusted p-value) and magnitude (log2FoldChange) of expression change between the WT samples versus the Tcf7 KO samples
  • Pathway analysis to show the relevant pathways that are associated with up and down-regulated genes.

Getting Started

Copy this folder into your home directory:

/gpfs1/cl/mmg3320/course_materials/R_tutorials/ENSG_counts
+ Open the file RNA-Seq_DESeq2_tutorial_EDIT.Rmd to start.

Install the following packages if you need to!

You will see an image like this if you are missing any packages required to run the .Rmd file 

#install.packages(c("knitr", "RColorBrewer", "pheatmap"))
BiocManager::install("apeglm") # Update all/some/none? [a/s/n] - type n

library(knitr)
library(DESeq2) 
library(RColorBrewer)
library(pheatmap)
library(ggplot2)
library(tidyverse)
library(apeglm)

Install these later on to generate the final image. These will take a bit longer to install. 

library(ggplotify)
library(ggpubr)
library(grid)
library(cowplot)
library(magick) # not sure this works on VACC-OOD

Citation

Shan, Q., Li, X., Chen, X. et al. Tcf1 and Lef1 provide constant supervision to mature CD8+ T cell identity and function by organizing genomic architecture. Nat Commun 12, 5863 (2021). https://doi.org/10.1038/s41467-021-26159-1