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Last data update: 2014.03.03

R: Function to use PWEA method on microarray or RNA-Seq data

PWEA	R Documentation

Function to use PWEA method on microarray or 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, preparePaths=TRUE, norm.method=NULL, test.method=NULL, tif=NULL, alpha=0.05, nperm=1000,  ncores=NULL, 
  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. Or a list of two data.frames: observed and random (after group permutations) of statistics of differential expression of genes
`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 data, `"MA"` for microarray, `"RNASeq"` for RNA-Seq or `"DEtable"` for a list of observed and random gene-level statistics
`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 TMM-normalization is performed. Possible values are: `"TMM", "DESeq2", "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"`.
`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(DEGraph)) {
  data("Loi2008_DEGraphVignette")
  pathways<-pathways("hsapiens","biocarta")[1:10]
  PWEA(exprLoi2008, classLoi2008, pathways,  type="MA", nperm=100)
}


if (require(gageData)) {
 data(hnrnp.cnts)
 hnrnp.cnts<-hnrnp.cnts[rowSums(hnrnp.cnts)>0,]
 group<-c(rep("sample",4), rep("control",4))
 pathways<-pathways("hsapiens","biocarta")[1:10]
 PWEA(hnrnp.cnts, group, pathways,  type="RNASeq", test="vstlimma", nperm=100)
}

## End(Not run)