geneSet2Net {GIGSEA} | R Documentation

## geneSet2Net

### Description

geneSet2Net transforms gene sets to a matrix, which represents the connectivity between terms and genes.

### Usage

    geneSet2Net(term, geneset, value = NULL, sep = ",")

### Arguments

`term` | a vector of character values incidating the names of gene sets, like pathway names and miRNA names.
`geneset` | a vector of character values, where each value is a gene list separated by 'sep'.
`value` | a vector of numeric values indicating the connectivity of between terms and genes. It could take either discrete values (0 and 1) or continuous vlaues.
`sep` | a character which separates the genes in the geneset.

### Value

a matrix of numeric values where the column corresponds to the term and the row corresponds to the geneset.

### Author(s)

Shijia Zhu, shijia.zhu@mssm.edu

### See Also

`geneSet2sparseMatrix`; `gmt2geneSet`;

### Examples

    # download the gmt file
    gmt <- readLines( paste0('http://amp.pharm.mssm.edu/CREEDS/download/',
    'single_drug_perturbations-v1.0.gmt') )

    # obtain the index of up-regulated and down-regulated gene sets
    index_up <- grep('-up',gmt)
    index_down <- grep('-dn',gmt)

    # transform the gmt file into gene sets. The gene set is a data frame,
    # comprising three vectors:
    # term (here is drug), geneset (a gene symbol list separate by comma),
    # and value (1 and -1 separate by comma)
    gff_up <- gmt2geneSet( gmt[index_up], termCol=c(1,2), singleValue = 1 )
    gff_down <- gmt2geneSet( gmt[index_down], termCol=c(1,2), singleValue = -1 )

    # combine up and down-regulated gene sets, and use 1 and -1 to indicate
    # their direction
    # extract the drug names
    term_up<-vapply( gff_up$term, function(x) gsub('-up','',x), character(1) )
    term_down<-vapply( gff_down$term, function(x) gsub('-dn','',x), character(1))
    all(term_up==term_down)

    # combine the up-regulated and down-regulated gene names for each
    # drug perturbation
    geneset <- vapply(1:nrow(gff_up),function(i) paste(gff_up$geneset[i],
    gff_down$geneset[i],sep=','), character(1) )

    # use 1 and -1 to indicate the direction of up and down-regulated genes
    value <- vapply( 1:nrow(gff_up) , function(i) paste(gff_up$value[i],
    gff_down$value[i],sep=',') , character(1) )

    # transform the gene set into matrix, where the row represents the gene,
    # the column represents the drug perturbation, and each entry takes values
    # of 1 and -1
    net1 <- geneSet2Net( term=term_up , geneset=geneset , value=value )
    # transform the gene set into sparse matrix, where the row represents the
    # gene, the column represents the drug perturbation, and each entry takes
    # values of 1 and -1
    net2 <- geneSet2sparseMatrix( term=term_up , geneset=geneset , value=value )
    tail(net1[,1:30])
    tail(net2[,1:30])
    # the size of sparse matrix is much smaller than the matrix
    format( object.size(net1), units = "auto")
    format( object.size(net2), units = "auto")

[Package GIGSEA version 1.8.0 Index]