PWEA {ToPASeq} | R Documentation

## PathWay Enrichment Analysis (PWEA)

### 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)

[Package ToPASeq version 1.20.0 Index]