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\name{qsTable}
\alias{qsTable}
\title{Summary of QSarray Results}
\description{
Print a table with a summary of the information on the most significant gene sets in QSarray.
}
\usage{
qsTable(QSarray, number=20, sort.by=c("fdr","p","logFC"))
}
\arguments{
\item{QSarray}{A QSarray object}
\item{number}{The number of gene sets to include in the table}
\item{sort.by}{character vector; a list of metrics to be used to sort the gene sets in QSarray. Can be any combination and order of \code{c("fdr","p","logFC")}, or \code{NULL} to specify no re-ordering of gene sets.}
}
\details{
This method will return a table with a summary of the results of qusage.
}
\value{
A data frame containing the following columns:
\itemize{
\item \code{pathway.name} - The name of the pathway
\item \code{log.fold.change} - Average log2 fold change value of the genes in the pathway
\item \code{p.Value} - The p-value for the gene set, as calculated using \code{pdf.pVal}
\item \code{FDR} - The Benjamini-Hochberg False Discovery rate. Calculated using R's built-in \code{p.adjust} method.
}
}
\examples{
##create example data
eset = matrix(rnorm(500*20),500,20, dimnames=list(1:500,1:20))
labels = c(rep("A",10),rep("B",10))
geneSets = list()
##create a number of gene sets with varying levels of differential expression.
for(i in 0:10){
genes = ((30*i)+1):(30*(i+1))
eset[genes,labels=="B"] = eset[genes,labels=="B"] + rnorm(1)
geneSets[[paste("Set",i)]] = genes
}
##calculate qusage results
results = qusage(eset,labels, "B-A", geneSets)
qsTable(results)
##show the first 5 sets, sorted by log fold change
qsTable(results, number=5, sort.by="logFC")
}
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