Over-Representation Analysis with ClusterProfiler

Create the object

go_enrich <- enrichGO(gene = genes,
                      universe = names(gene_list),
                      OrgDb = organism, 
                      keyType = 'ENSEMBL',
                      readable = T,
                      ont = "BP",
                      pvalueCutoff = 0.05, 
                      qvalueCutoff = 0.10)

Upset Plot

Emphasizes the genes overlapping among different gene sets.

#BiocManager::install("enrichplot")
library(enrichplot)
upsetplot(go_enrich)

Wordcloud

wcdf<-read.table(text=go_enrich$GeneRatio, sep = "/")[1]
wcdf$term<-go_enrich[,2]
wordcloud(words = wcdf$term, freq = wcdf$V1, scale=(c(4, .1)), colors=brewer.pal(8, "Dark2"), max.words = 25)

Barplot

barplot(go_enrich, 
        drop = TRUE, 
        showCategory = 10, 
        title = "GO Biological Pathways",
        font.size = 8)

Dotplot

dotplot(go_enrich)

Encrichment map:

Enrichment map organizes enriched terms into a network with edges connecting overlapping gene sets. In this way, mutually overlapping gene sets are tend to cluster together, making it easy to identify functional modules.

emapplot(go_enrich)

Enriched GO induced graph:

goplot(go_enrich, showCategory = 10)

Category Netplot

The cnetplot depicts the linkages of genes and biological concepts (e.g. GO terms or KEGG pathways) as a network (helpful to see which genes are involved in enriched pathways and genes that may belong to multiple annotation categories).

# categorySize can be either 'pvalue' or 'geneNum'
cnetplot(go_enrich, categorySize="pvalue", foldChange=gene_list)

KEGG Pathway Enrichment

For KEGG pathway enrichment using the gseKEGG() function, we need to convert id types. We can use the bitr function for this (included in clusterProfiler). It is normal for this call to produce some messages / warnings.

In the bitr function, the param fromType should be the same as keyType from the gseGO function above (the annotation source). This param is used again in the next two steps: creating dedup_ids and df2.

toType in the bitr function has to be one of the available options from keyTypes(org.Dm.eg.db) and must map to one of ‘kegg’, ‘ncbi-geneid’, ‘ncib-proteinid’ or ‘uniprot’ because gseKEGG() only accepts one of these 4 options as it’s keytype parameter. In the case of org.Dm.eg.db, none of those 4 types are available, but ‘ENTREZID’ are the same as ncbi-geneid for org.Dm.eg.db so we use this for toType.

As our intial input, we use original_gene_list which we created above.

Prepare Data

# Convert gene IDs for enrichKEGG function
# We will lose some genes here because not all IDs will be converted
ids<-bitr(names(original_gene_list), fromType = "ENSEMBL", toType = "ENTREZID", OrgDb="org.Dm.eg.db") # remove duplicate IDS (here I use "ENSEMBL", but it should be whatever was selected as keyType)
dedup_ids = ids[!duplicated(ids[c("ENSEMBL")]),]

# Create a new dataframe df2 which has only the genes which were successfully mapped using the bitr function above
df2 = df[df$X %in% dedup_ids$ENSEMBL,]

# Create a new column in df2 with the corresponding ENTREZ IDs
df2$Y = dedup_ids$ENTREZID

# Create a vector of the gene unuiverse
kegg_gene_list <- df2$log2FoldChange

# Name vector with ENTREZ ids
names(kegg_gene_list) <- df2$Y

# omit any NA values 
kegg_gene_list<-na.omit(kegg_gene_list)

# sort the list in decreasing order (required for clusterProfiler)
kegg_gene_list = sort(kegg_gene_list, decreasing = TRUE)

# Exctract significant results from df2
kegg_sig_genes_df = subset(df2, padj < 0.05)

# From significant results, we want to filter on log2fold change
kegg_genes <- kegg_sig_genes_df$log2FoldChange

# Name the vector with the CONVERTED ID!
names(kegg_genes) <- kegg_sig_genes_df$Y

# omit NA values
kegg_genes <- na.omit(kegg_genes)

# filter on log2fold change (PARAMETER)
kegg_genes <- names(kegg_genes)[abs(kegg_genes) > 2]

Create enrichKEGG object

organism KEGG Organism Code: The full list is here: https://www.genome.jp/kegg/catalog/org_list.html (need the 3 letter code). I define this as kegg_organism first, because it is used again below when making the pathview plots.
keyType one of ‘kegg’, ‘ncbi-geneid’, ‘ncib-proteinid’ or ‘uniprot’.

kegg_organism = "dme"
kk <- enrichKEGG(gene=kegg_genes, universe=names(kegg_gene_list),organism=kegg_organism, pvalueCutoff = 0.05, keyType = "ncbi-geneid")

Barplot

barplot(kk, 
        showCategory = 10, 
        title = "Enriched Pathways",
        font.size = 8)

Dotplot

dotplot(kk, 
        showCategory = 10, 
        title = "Enriched Pathways",
        font.size = 8)

Category Netplot:

The cnetplot depicts the linkages of genes and biological concepts (e.g. GO terms or KEGG pathways) as a network (helpful to see which genes are involved in enriched pathways and genes that may belong to multiple annotation categories).

# categorySize can be either 'pvalue' or 'geneNum'
cnetplot(kk, categorySize="pvalue", foldChange=gene_list)