Exercise 1 - pathview

Load the required packages and data for Day 11 if you have not already done so.

library(msigdbr)
library(clusterProfiler)
library(pathview)
library(tidyverse)

shrink.d11 <- readRDS("RObjects/Shrunk_Results.d11.rds")
  1. Use pathview to export a figure for “mmu04659”or “mmu04658”, but this time only use genes that are statistically significant at FDR < 0.01
logFC <- shrink.d11 %>% 
  drop_na(FDR, Entrez) %>% 
  filter(FDR < 0.01) %>% 
  pull(logFC, Entrez) 

pathview(gene.data = logFC, 
         pathway.id = "mmu04659", 
         species = "mmu", 
         limit = list(gene=5, cpd=1))
## Info: Downloading xml files for mmu04659, 1/1 pathways..
## Info: Downloading png files for mmu04659, 1/1 pathways..
## 'select()' returned 1:1 mapping between keys and columns
## Info: Working in directory /Users/sawle01/Documents/training/Bulk_RNAseq_Course_Base/Markdowns
## Info: Writing image file mmu04659.pathview.png

mmu04659.pathview.png:

mmu04659 - Th17 cell differentiation

Exercise 2 - GO term enrichment analysis

clusterProfiler can also perform over-representation analysis on GO terms. using the commmand enrichGO. Look at the help page for the command enrichGO (?enrichGO) and have a look at the instructions in the clusterProfiler book.

  1. Run the over-representation analysis for GO terms
    • Use genes that have an adjusted p-value (FDR) of less than 0.01 and an absolute fold change greater than 2.
    • For this analysis you can use Ensembl IDs rather then Entrez
    • You’ll need to provide the background (universe) genes, this should be all the genes in our analysis.
    • The mouse database package is called org.Mm.eg.db. You’ll need to load it using library before running the analysis.
    • As we are using Ensembl IDs, you’ll need to set the keyType parameter in the enrichGO command to indicate this.
    • Only test terms in the “Biological Processes” ontology
library(org.Mm.eg.db)

sigGenes <-  shrink.d11 %>% 
    drop_na(FDR) %>% 
    filter(FDR < 0.01 & abs(logFC) > 1) %>% 
    pull(GeneID)

universe <- shrink.d11$GeneID

ego <- enrichGO(gene          = sigGenes, 
                universe      = universe,
                OrgDb         = org.Mm.eg.db,
                keyType       = "ENSEMBL",
                ont           = "BP",
                pvalueCutoff  = 0.01,
                readable      = TRUE)
  1. Use the dotplot function to visualise the results.
dotplot(ego, font.size = 14)

Exercise 3 - GSEA

Another common way to rank the genes is to order by pvalue, but also, sorting so that upregulated genes are at the start and downregulated at the end - you can do this combining the sign of the fold change and the pvalue.

First load the pathway details if you have not already done so.

library(msigdbr)
term2gene <- msigdbr(species = "Mus musculus", category = "H") %>% 
  select(gs_name, entrez_gene)
term2name <- msigdbr(species = "Mus musculus", category = "H") %>% 
  select(gs_name, gs_description) %>% 
  distinct()
  1. Rank the genes by statistical significance - you will need to create a new ranking value using -log10({p value}) * sign({Fold Change}).
# rank genes
rankedGenes.e11 <- shrink.d11 %>%
  drop_na(Entrez, pvalue, logFC) %>%
  mutate(rank = -log10(pvalue) * sign(logFC)) %>%
  arrange(desc(rank)) %>%
  pull(rank, Entrez)
  1. Run fgsea using the new ranked genes and the Hallmark pathways.
# conduct analysis:
gseaRes.e11 <- GSEA(rankedGenes.e11,
                TERM2GENE = term2gene,
                TERM2NAME = term2name,
                pvalueCutoff = 1.00, 
                minGSSize = 15,
                maxGSSize = 500)
## preparing geneSet collections...
## GSEA analysis...
## Warning in fgseaMultilevel(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.
## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are less
## than 1e-10. You can set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...

View the results:

as_tibble(gseaRes.e11) %>% 
  arrange(desc(abs(NES))) %>% 
  top_n(10, wt=-p.adjust) %>% 
  select(-core_enrichment) %>%
  mutate(across(c("enrichmentScore", "NES"), round, digits=3)) %>% 
  mutate(across(c("pvalue", "p.adjust", "qvalues"), scales::scientific))
  1. Conduct the same analysis for the day 33 Infected vs Uninfected contrast.
# read d33 data in:
shrink.d33 <- readRDS("RObjects/Shrunk_Results.d33.rds")

# rank genes
rankedGenes.e33 <- shrink.d33 %>%
  drop_na(Entrez, pvalue, logFC) %>%
  mutate(rank = -log10(pvalue) * sign(logFC)) %>%
  arrange(desc(rank)) %>%
  pull(rank,Entrez)

# perform analysis
gseaRes.e33 <- GSEA(rankedGenes.e33,
                TERM2GENE = term2gene,
                TERM2NAME = term2name,
                pvalueCutoff = 1.00, 
                minGSSize = 15,
                maxGSSize = 500)
## preparing geneSet collections...
## GSEA analysis...
## Warning in fgseaMultilevel(...): There were 2 pathways for which P-values were
## not calculated properly due to unbalanced (positive and negative) gene-level
## statistic values. For such pathways pval, padj, NES, log2err are set to NA. You
## can try to increase the value of the argument nPermSimple (for example set it
## nPermSimple = 10000)
## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are less
## than 1e-10. You can set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...

View the results:

as_tibble(gseaRes.e33) %>% 
  arrange(desc(abs(NES))) %>% 
  top_n(10, wt=-p.adjust) %>% 
  select(-core_enrichment) %>%
  mutate(across(c("enrichmentScore", "NES"), round, digits=3)) %>% 
  mutate(across(c("pvalue", "p.adjust", "qvalues"), scales::scientific))