RankProd (version 2.44.0)

topGene: Output Significant Genes

Description

Identify differentially expressed genes using rank product method

Usage

topGene(x,cutoff=NULL,method="pfp",num.gene=NULL,logged=TRUE,logbase=2,gene.names=NULL)

Arguments

x

the value returned by the function RP, RP.advance or Rsum.advance

cutoff

threshold in pfp used to select genes

method

If cutoff is provided, the method needs to be selected to identify genes."pfp" uses percentage of false prediction, which is the default setting. "pval" used p-value which is less stringent than pfp

num.gene

number of candidate genes of interests, if cutoff is provided, this will be ignored

logged

if "TRUE", data has bee logged, otherwise set it to "FALSE"

logbase

base used when taking log, used to restore the fold change.The default value is 2, this will be ignored if logged=FALSE

gene.names

if "NULL", no gene name will be attached to the output table

Value

Two tables of identified genes with gene.index: index of gene in the original data set RP/Rsum: Computed rank product/sum for each gene FC:(class1/class2): Expression Fold change of class 1/ class 2. pfp: estimated pfp for each gene if the gene is used as cutoff point P.value: estimated p-value for each geneTable 1 list genes that are up-regulated under class 2, Table 1 ist genes that are down-regulated under class 2,

References

Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92

See Also

plotRP RP RPadvance RSadvance

Examples

      # Load the data of Golub et al. (1999). data(golub) 
      # contains a 3051x38 gene expression
      # matrix called golub, a vector of length called golub.cl 
      # that consists of the 38 class labels,
      # and a matrix called golub.gnames whose third column 
      # contains the gene names.
      data(golub)

      #use a subset of data as example, apply the rank 
      #product method
      subset <- c(1:4,28:30)
      #Setting rand=123, to make the results reproducible,

      #identify genes 
      RP.out <- RP(golub[,subset],golub.cl[subset],rand=123)  

      #get two lists of differentially expressed genes 
      #by setting FDR (false discivery rate) =0.05

      table=topGene(RP.out,cutoff=0.05,method="pfp",logged=TRUE,logbase=2,
                   gene.names=golub.gnames[,3])
      table$Table1
      table$Table2

      #using pvalue<0.05
      topGene(RP.out,cutoff=0.05,method="pval",logged=TRUE,logbase=2,
                   gene.names=golub.gnames[,3])

      #by selecting top 10 genes

      topGene(RP.out,num.gene=10,gene.names=golub.gnames[,3])