PWEA: PathWay Enrichment Analysis (PWEA)

In ToPASeq: Topology-based pathway analysis of RNA-seq data

Description

The function runs PWEA method (please see References for the details) on gene expression data matrix, vector specifing to which group a sample belongs and a list of pathway graphs. Briefly, it is a weighted GSEA-like method. The weightes are based on the distance and Pearson's correlation between genes in a pathway.

Usage

PWEA(x, group, pathways, type, which = "proteins", edgeType = NULL,
  preparePaths = TRUE, norm.method = NULL, test.method = NULL,
  tif = NULL, alpha = 0.05, nperm = 1000, ncores = 1,
  both.directions = TRUE, maxNodes = 150, minEdges = 0,
  commonTh = 2, filterSPIA = FALSE, convertTo = "none",
  convertBy = NULL)

Arguments

x	An ExpressionSet object or a gene expression data matrix or count matrix, rows refer to genes, columns to samples
group	Name or number of the phenoData column or a character vector or factor that contains required class assigments
pathways	A list of pathways in a form from graphite package or created by preparePathways()
type	Type of the input data, "MA" for microarray and "RNASeq" for RNA-Seq
which	Character, which type of nodes is preserved in a pathway. Possible values are "proteins","metabolites","mixed"
edgeType	Character, which type of edges is preserved in a pathway. If NULL, all edges are kept.
preparePaths	Logical, by default the pathways are transformed with preparePathways(). Use FALSE, if you have done this transformation separately
norm.method	Character, the method to normalize RNAseq data. If NULL then vst-normalization is performed. Possible values are: "edgeR", "vst", "rLog", "none"
test.method	Character, the method for differentiall expression analysis of RNAseq data. If NULL then "voomlimma" is used. Possible values are: "DESeq2", "voomlimma", "vstlimma", "edgeR". This analysis is needed only for the visualization.
tif	A list of Topology Influence Factor's. One slot refers to one pathway. Use prepareTIF() to create it. It is required only if type=="DEtable"
alpha	Numeric, a theshold value used during TIF calculation
nperm	Numeric, number of permutations. Used only if x %in% c("MA", "RNASeq")
ncores	Numeric, number of cores. Used only if x %in% c("MA", "RNASeq"). The permutations are calculated in parallel way
both.directions, maxNodes, minEdges, commonTh, filterSPIA, convertTo, convertBy	Arguments for the preparePathways()

Value

A list:

res	A data frame, rows refer to pathways. It contains: Enrichment score for a pathway, p-value and p-value adjusted for multiple hypothesis testing by Benjamini-Hochberg's FDR method. NA's if less than 2 nodes are present in the data
topo.sig	A list, topology influence factors for the genes in individual pathways. NULL if less than 2 nodes are present in the data
degtest	A named vector of statistics from testing the differential expression

Author(s)

Ivana Ihnatova

References

Hung, JH., Whitfield, T. W., Yang, TH., Hu, Z., Weng, Z., DeLisi, Ch. (2010) Identification of functional modules that correlate with phenotypic difference: the influence of network topology, Genome Biology, 11:R23

Examples

## Not run: 
if (require(breastCancerVDX)) {
data("vdx")
pathways<-pathways("hsapiens","biocarta")[1:3]
MAdata<-Biobase::exprs(vdx)[,1:10]
rownames(MAdata)<-Biobase::fData(vdx)[,"Gene.symbol"]
MAdata<-MAdata[!duplicated(rownames(MAdata)),]

PWEA(MAdata, Biobase::pData(vdx)[,"er"][1:10], pathways, type="MA", convertTo="SYMBOL", nperm=10)
}

## End(Not run)

ToPASeq documentation built on Nov. 8, 2020, 4:59 p.m.