Gene Ontology testing with clusterProfiler

Overview

Questions

• What are the different types of GO terms (BP, MF, CC)?
• How do we perform ORA using enrichGO() function?
• How can we run GSEA-style functional class scoring with gseGO() function?

Objectives

• Apply GO-based enrichment methods using clusterProfiler
• Perform both ORA and GSEA using the GO terms database
• Build confidence in navigating GO resources and interpreting enriched terms

Introduction

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The Gene Ontology (GO) project is a major bioinformatics initiative that standardises how we describe gene functions across species, organising them into three categories: Biological Process, Molecular Function and Cellular Component. clusterProfiler is an R package that allows us to test whether these GO terms are associated with our RNA-seq results and gain insight into the pathways or functions represented in our data. This section demonstrates how to perform both over-representation analysis (ORA) and functional class scoring (FCS) with GO database, depending on whether you are working with a list of significant genes or full ranked expression data.

Over-Representation Analysis (ORA)

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ORA tests whether a list of significant genes are linked to specific GO terms. The input is a vector of gene IDs (or list of genes) that passes your differential expression cut-off. ORA can be run separately for downregulated and upregulated genes to reveal which GO terms are enriched in each direction.

We first subset the debasal dataset to extract genes with adjusted p-value below 0.01 and store this set of significant genes in an object called genes. We then run enrichGO function using this gene list, specifying the organism database org.Mm.eg.db, the identifier type ENTREZID and the GO category of interest CC (for cellular component). The function is configured with standard p-value and q-value, using Benjamini-Hochberg correction. We use the function head() to check the first few lines of output.

ERROR

Error in `library()`:
! there is no package called 'edgeR'

ERROR

Error in `library()`:
! there is no package called 'goseq'

ERROR

Error in `library()`:
! there is no package called 'fgsea'

ERROR

Error in `library()`:
! there is no package called 'EGSEA'

ERROR

Error in `library()`:
! there is no package called 'clusterProfiler'

ERROR

Error in `library()`:
! there is no package called 'org.Mm.eg.db'

ERROR

Error in `library()`:
! there is no package called 'ggplot2'

ERROR

Error in `library()`:
! there is no package called 'enrichplot'

ERROR

Error in `library()`:
! there is no package called 'pathview'

ERROR

Error in `library()`:
! there is no package called 'edgeR'

ERROR

Error in `library()`:
! there is no package called 'impute'

ERROR

Error in `library()`:
! there is no package called 'preprocessCore'

ERROR

Error in `library()`:
! there is no package called 'RegEnrich'

R

debasal$Status <- debasal$adj.P.Val < 0.01
gene <- debasal$ENTREZID[debasal$Status]

ego <- enrichGO(gene = gene,
                OrgDb = org.Mm.eg.db,
                keyType = 'ENTREZID',
                ont = "CC",
                pAdjustMethod = "BH",
                pvalueCutoff = 0.01,
                qvalueCutoff = 0.05,
                readable = TRUE)

ERROR

Error in `enrichGO()`:
! could not find function "enrichGO"

R

head(ego)

ERROR

Error:
! object 'ego' not found

We can then use dotplot() function to visualise the results in the form of a dot plot. From the plot below, we can see that GO term cellular component spindle, membrane microdomain and ribosome are top enriched terms.

R

dotplot(ego)

ERROR

Error in `dotplot()`:
! could not find function "dotplot"

Discussion

Challenge

Challenge! Can you identify enriched GO term biological process in deluminal dataset? Are the enriched pathways similar?

Gene Set Enrichment Analysis (GSEA)

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We can also perform GSEA using GO database. GSEA is a type of functional class scoring method that evaluates whether genes belonging to a GO term tend to appear at the top or bottom of a ranked gene list, rather than relying on a cut-off (i.e. adj.P.Val < 0.01). The input is a continuous ranking metric (e.g. log2FC) for all genes. This allows the detection of subtle but coordinated shifts in GO terms for both downregulated and upregulated pathways.

We begin by creating a ranked gene list for GSEA by extracting the logFC values from debasal dataset and its corresponding ENTREZID. We then sort this vector in a decreasing order so that the upregulated genes appear at the top of the list and the downregulated genes at the bottom. Using this ranked gene list, we run gseGO() to perform GSEA on GO terms CC, by specifying the organism database, gene ID type, gene set limits and p-value cut-off for enrichment.

R

debasal_genelist <- debasal$logFC
names(debasal_genelist) <- debasal$ENTREZID
debasal_genelist <- sort(debasal_genelist, decreasing = TRUE)

ego3 <- gseGO(gene          = debasal_genelist,
                OrgDb         = org.Mm.eg.db,
                keyType       = 'ENTREZID',
                ont           = "CC",
              minGSSize    = 100,
              maxGSSize    = 500,
              pvalueCutoff = 0.05,
              verbose      = FALSE)

ERROR

Error in `gseGO()`:
! could not find function "gseGO"

R

head(ego3)

ERROR

Error:
! object 'ego3' not found

R

dotplot(ego3)

ERROR

Error in `dotplot()`:
! could not find function "dotplot"

We can also use the gseaplot() function to visualise GSEA result for a specific gene set. In this example, we select the top-ranked enriched GO term (geneSetID = 1). The result-ing plot displays how genes contributing to the enrichment of this GO term are distributed in the ranked gene list.

R

gseaplot(ego3, by = "all", title = ego3$Description[1], geneSetID = 1)

ERROR

Error in `gseaplot()`:
! could not find function "gseaplot"

Key Points

• GO terms are divided into Biological Process (BP), Molecular Function (MF) and Cellular Component (CC), which can be analysed separately or together depending on the biological question.
• The enrichGO() and gseGO() functions in clusterProfiler allow users to perform ORA and GSEA using the GO database directly.
• GO testing results highlight gene sets or pathways that are overrepresented in your dataset, allowing interpretation of downregulated or upregulated genes.