qsTable {qusage} | R Documentation |
Print a table with a summary of the information on the most significant gene sets in QSarray.
qsTable(QSarray, number=20, sort.by=c("fdr","p","logFC"))
QSarray |
A QSarray object |
number |
The number of gene sets to include in the table |
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 |
This method will return a table with a summary of the results of qusage.
A data frame containing the following columns:
pathway.name
- The name of the pathway
log.fold.change
- Average log2 fold change value of the genes in the pathway
p.Value
- The p-value for the gene set, as calculated using pdf.pVal
FDR
- The Benjamini-Hochberg False Discovery rate. Calculated using R's built-in p.adjust
method.
##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")