runPathwayAnalysis {RCPA}

Topology-based Pathway Analysis

Description

This function performs patwhay analysis using SPIA, CePaORA, and CePaGSA methods.

Usage

runPathwayAnalysis(
  summarizedExperiment,
  network,
  method = c("spia", "cepaORA", "cepaGSA"),
  SPIAArgs = list(all = NULL, nB = 2000, verbose = TRUE, beta = NULL, combine = "fisher",
    pThreshold = 0.05),
  CePaORAArgs = list(bk = NULL, cen = c("equal.weight", "in.degree", "out.degree",
    "betweenness", "in.reach", "out.reach"), cen.name = c("equal.weight", "in.degree",
    "out.degree", "betweenness", "in.reach", "out.reach"), iter = 1000, pThreshold =
    0.05),
  CePaGSAArgs = list(cen = c("equal.weight", "in.degree", "out.degree", "betweenness",
    "in.reach", "out.reach"), cen.name = c("equal.weight", "in.degree", "out.degree",
    "betweenness", "in.reach", "out.reach"), nlevel = "tvalue_abs", plevel = "mean", iter
    = 1000)
)

Arguments

summarizedExperiment

The generated SummarizedExpriment object from DE analysis result.

network

The pathways network object.

method

The pathway analsyis method, including SPIA, cepaORA, and cepaGSA.

SPIAArgs

A list of other passed arguments to spia. See spia function.

CePaORAArgs

A list of other passed arguments to CePaORA. See CePa function.

CePaGSAArgs

A list of other passed arguments to CePaGSA. See CePa function.

Value

A dataframe of pathway analysis result, which contains the following columns

• ID: The ID of the gene set

• p.value: The p-value of the gene set

• pFDR: The adjusted p-value of the gene set using the Benjamini-Hochberg method

• score: The enrichment score of the gene set

• normalizedScore: The normalized enrichment score of the gene set

• sampleSize: The total number of samples in the study

• name: The name of the gene set

• pathwaySize: The size of the gene set

Examples

library(RCPA)
RNASeqDEExperiment <- loadData("RNASeqDEExperiment")
spiaNetwork <- loadData("spiaNetwork")
cepaNetwork <- loadData("cepaNetwork")

spiaResult <- runPathwayAnalysis(RNASeqDEExperiment, spiaNetwork, method = "spia")
cepaORAResult <- runPathwayAnalysis(RNASeqDEExperiment, cepaNetwork, method = "cepaORA")