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Perform Enrichment analysis on differentially expressed genes or phospho-sites

Source: R/enrichment.R
enrichDifferential.Rd

enrichDifferential performs enrichment analysis on differentially expressed genes and phospho-sites for either pathway or phospho-specific enrichment, depending on the input parameters. It supports multiple statistical methods such as PAGE and GSEA for pathway enrichment and a Kolmogorov-Smirnov approach for phospho-enrichment.

Usage

enrichDifferential(
  dea,
  type = c("Pathway enrichment", "Phospho-signature enrichment"),
  gsaMethod = c("PAGE", "GSEA"),
  geneSet,
  ptmSet,
  statType = c("stat", "log2FC"),
  nPerm = 100,
  sigLevel = 0.05,
  ifFDR = FALSE
)

Arguments

dea

A data frame containing the differential expression analysis results. It should include columns like `pvalue`, `Gene` (or `site`), `stat`, and `log2FC`.

type

A character string indicating the type of enrichment. Options are `"Pathway enrichment"` or `"Phospho-signature enrichment"`.

gsaMethod

A character string specifying the gene set analysis method for pathway enrichment. Options are `"PAGE"` or `"GSEA"`.

geneSet

A gene set collection to use for pathway enrichment.

ptmSet

A post-translational modification (PTM) set database for phospho-enrichment analysis.

statType

A character string specifying the statistic type to use. Options are `"stat"` or `"log2FC"`.

nPerm

A numeric specifying the number of permutations for GSEA. Default is 100.

sigLevel

A numeric value representing the significance threshold for filtering results. Ddefault is 0.05.

ifFDR

A logical value indicating whether to filter results using FDR-adjusted p-values. Default is `FALSE`.

Value

A data frame containing the results of the enrichment analysis, including columns such as the gene set name, statistical significance, and adjusted p-values.

Details

The `enrichDifferential` function performs either pathway enrichment or phospho-enrichment analysis based on the `type` parameter. For pathway enrichment, it uses either the PAGE or GSEA method with a provided gene set collection. For phospho-enrichment, it uses a Kolmogorov-Smirnov test with a PTM set database. Results can be filtered by significance level and optionally adjusted for FDR.

Examples

library(SummarizedExperiment)
library(piano)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
# Preprocess the proteome assay
result <- preprocessPhos(se, normalize = TRUE)
# Call the function to perform differential expression analyis
dea <- performDifferentialExp(se = result, assay = "Intensity",
method = "limma", reference = "1stCrtl", target = "EGF",
condition = "treatment")
# Load the gene set
genesetPath <- appDir <- system.file("shiny-app/geneset",
package = "SmartPhos")
inGMT <- loadGSC(paste0(genesetPath,"/Cancer_Hallmark.gmt"),type="gmt")
# Call the function
resTab <- enrichDifferential(dea = dea$resDE, type = "Pathway enrichment",
gsaMethod = "PAGE", geneSet = inGMT, statType = "stat", nPerm = 200,
sigLevel = 0.05, ifFDR = FALSE)
#> Running gene set analysis:
#> Checking arguments...done!
#> Final gene/gene-set association: 20 genes and 22 gene sets
#>   Details:
#>   Calculating gene set statistics from 20 out of 65 gene-level statistics
#>   Removed 4366 genes from GSC due to lack of matching gene statistics
#>   Removed 28 gene sets containing no genes after gene removal
#>   Removed additionally 0 gene sets not matching the size limits
#>   Loaded additional information for 22 gene sets
#> Calculating gene set statistics...done!
#> Calculating gene set significance...done!
#> Adjusting for multiple testing...done!