An introduction to _aPEAR_

In aPEAR: Advanced Pathway Enrichment Analysis Representation

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aPEAR is designed to help you notice the most important biological themes in your enrichment analysis results. It analyses the gene lists of the pathways and detects clusters of redundant overlapping gene sets.

Let's begin by performing a simple gene set enrichment analysis with clusterProfiler:

# Load all the packages:
library(data.table)
library(ggplot2)
library(dplyr)
library(stringr)
library(clusterProfiler)
library(DOSE)
library(org.Hs.eg.db)
library(aPEAR)
data(geneList)

# Perform enrichment using clusterProfiler
set.seed(42)
enrich <- gseGO(geneList, OrgDb = org.Hs.eg.db, ont = 'CC')

Generate an enrichment network with enrichmentNetwork()

enrichmentNetwork is the most important function exported by aPEAR. It detects clusters of similar pathways and generates a ggplot2 visualization. The only thing it asks you to provide is your enrichment result:

set.seed(654824)
enrichmentNetwork(enrich@result)

Internally, enrichmentNetwork calls two functions, findPathClusters and plotPathClusters, which are described in more detail below.

What if I performed my enrichment analysis using another method, not clusterProfiler?

aPEAR currently recognizes input from clusterProfiler and gProfileR. However, if you have custom enrichment input, do not worry!

aPEAR accepts any kind of enrichment input as long as it is formatted correctly, the only requirement is that the gene list of each pathway is known. You should format your data so that:

• it is a data.frame.
• it has a column titled Description - it will be used to label each node and to select the name of the most important cluster.
• it has a column titled pathwayGenes which contains the gene list of each pathway - it will be used to calculate the similarities between the pathways. It can be leading edge genes or the full gene list. The ID type (Ensembl, Gene symbol, etc.) does not matter but should be the same between all the pathways. The genes should be separated by "/".
• a column for colouring the nodes - it should be specified with the parameter colorBy.
• a column for setting the node size - it should be specified with the parameter nodeSize.

For example, you might format your data like this:

enrichmentData <- enrich@result %>%
  as.data.table() %>%
  .[ 1:5 ] %>%
  .[ , list(Description, pathwayGenes = core_enrichment, NES, Size = setSize) ] %>%
  .[ , pathwayGenes := str_trunc(pathwayGenes, 20) ]

enrichmentData[ 1:5 ]

enrichmentData <- enrich@result %>%
  as.data.table() %>%
  .[ , list(Description, pathwayGenes = core_enrichment, NES, Size = setSize) ]

Then, tell the enrichmentNetwork what to do:

p <- enrichmentNetwork(enrichmentData, colorBy = 'NES', nodeSize = 'Size', verbose = TRUE)

What if I performed ORA and do not have the normalized enrichment score (NES)?

Good news: you can use the p-values to color the nodes! Just specify the colorBy column and colorType = 'pval':

set.seed(348934)
enrichmentNetwork(enrich@result, colorBy = 'pvalue', colorType = 'pval', pCutoff = -5)

Find pathway clusters with findPathClusters()

If your goal is only to obtain the clusters of redundant pathways, the function findPathClusters is the way to go. It accepts a data.frame with the enrichment results and returns a list of the pathway clusters and the similarity matrix:

clusters <- findPathClusters(enrich@result, cluster = 'hier', minClusterSize = 6)

clusters$clusters[ 1:5 ]

pathways <- clusters$clusters[ 1:5, Pathway ]
clusters$similarity[ pathways, pathways ]

For more information about available similarity metrics, clustering methods, cluster naming conventions, and other available parameters, see ?aPEAR.theme.

Visualize pathway clusters with plotPathClusters()

To visualize clustering results obtained with findPathClusters, use the function plotPathClusters:

set.seed(238923)
plotPathClusters(
  enrichment = enrich@result,
  sim = clusters$similarity,
  clusters = clusters$clusters,
  fontSize = 4,
  outerCutoff = 0.01, # Decrease cutoff between clusters and show some connections
  drawEllipses = TRUE
)

For more parameter options, see ?aPEAR.theme.

aPEAR documentation built on July 9, 2023, 6:16 p.m.