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Perform Cluster Enrichment Analysis

Source: R/enrichment.R
clusterEnrich.Rd

clusterEnrich performs enrichment analysis on gene clusters, using Fisher's Exact Test to determine the significance of enrichment for each cluster.

Usage

clusterEnrich(
  clusterTab,
  se,
  inputSet,
  reference = NULL,
  ptm = FALSE,
  adj = "BH",
  filterP = 0.05,
  ifFDR = FALSE
)

Arguments

clusterTab

A data frame containing cluster information, where each row corresponds to a gene and its assigned cluster.

se

A SummarizedExperiment object containing gene expression data and metadata.

inputSet

A list or data frame of gene sets to be used for enrichment analysis.

reference

A character vector of reference genes. If NULL, it will be extracted from se object. Default is NULL.

ptm

Logical. If TRUE, the function will perform enrichment analysis on post-translational modification (PTM) gene sets. Default is FALSE.

adj

Character. The method for adjusting p-values. Default is "BH".

filterP

Numeric. The p-value threshold for filtering significant results. Default is 0.05.

ifFDR

Logical. If TRUE, the function will use FDR-adjusted p-values for significance filtering. Default is FALSE.

Value

A list containing two elements:

  • `table`: A data frame with enrichment results for each cluster and pathway.

  • `plot`: A ggplot2 object showing the significance of enrichment for each pathway across clusters.

Details

The function first retrieves or computes the reference set of genes or PTM sites. It then performs enrichment analysis for each cluster using the runFisher function. The results are filtered based on the p-value threshold and adjusted for multiple testing if ifFDR is TRUE. The function generates a dot plot where the size and color of the points represent the significance of enrichment.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
seProcess <- preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")
#> Imputing along margin 2 (samples/columns).
result <- addZeroTime(seProcess, condition = "treatment", treat = "EGF",
zeroTreat = "1stCrtl", timeRange = c("20min","40min", "6h"))
# Get the numeric matrix
exprMat <- SummarizedExperiment::assay(result)
# Call the clustering function
clust <- clusterTS(x = exprMat, k = 3)
genesetPath <- appDir <- system.file("shiny-app/geneset",
package = "SmartPhos")
inGMT <- piano::loadGSC(paste0(genesetPath,
"/Cancer_Hallmark.gmt"),type="gmt")
# Call the function
clusterEnrich(clust$cluster, seProcess, inGMT)
#> $table
#> # A tibble: 0 × 9
#> # ℹ 9 variables: Name <chr>, Gene.number <int>, Set.size <int>, pval <dbl>,
#> #   Genes <list>, padj <dbl>, cluster <chr>, ifSig <lgl>, atLeast1 <lgl>
#> 
#> $plot

#>