Talk:RP.R

RP package:RankProd R Documentation

Rank Product Analysis of Microarray

Description:

    Perform rank product method to identify differentially  expressed
    genes. It is possible to do either a one-class or two-class
    analysis.

Usage:

        RP(data,cl,num.perm=100,logged=TRUE,
           na.rm=FALSE,gene.names=NULL,plot=FALSE, rand=NULL)

Arguments:

   data: the data set that should be analyzed. Every  row of this data
         set must correspond to a gene.

     cl: a vector containing the class labels of the samples. In the
         two class unpaired case, the label of a  sample is either 0
         (e.g., control group) or 1  (e.g., case group). For one class
          data, the label for  each sample should be 1.

num.perm: number of permutations used in the calculation of the null

         density. Default is 'num.perm=100'.

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

  na.rm: if 'FALSE' (default), the NA value will not be used in
         computing rank. If 'TRUE', the missing  values will be
         replaced by the gene-wise mean of the non-missing values.
         Gene with all values missing  will be assigned "NA"

gene.names: if "NULL", no gene name will be assigned to the estimated

         percentage of  false positive predictions (pfp).

   plot: If "TRUE", plot the estimated pfp verse the  rank of each
         gene.

   rand: if specified, the random number generator will  be put in a
         reproducible state using the rand value as seed.

Value:

    A result of identifying differentially expressed genes  between
    two classes. The identification consists of two parts, the
    identification of  up-regulated  and down-regulated genes in 
    class 2 compared to class 1, respectively.

    pfp: estimated percentage of false positive predictions (pfp) up
         to  the position of each gene under two  identificaiton each

   pval: estimated pvalue for each gene being up- and down-regulated

    RPs: Original rank-product of each genes for two  dentificaiton
         each 

 RPrank: rank of the rank product of each genes

Orirank: original rank in each comparison, which  is used to construct
         rank product

  AveFC: fold change of average expression under class 1 over  that
         under class 2. log-fold change if data is in log  scaled,
         original fold change if data is unlogged. 

Note:

    Percentage of false prediction (pfp), in theory, is  equivalent of
    false  discovery rate (FDR), and it is possible to be large than
    1.

    The function looks for up- and down- regulated genes in two
    seperate steps, thus two pfps and pvalues are computed and used to
    identify  gene that belong to each group.   

    This function is suitable to deal with data from a  single origin,
    e.g. single  experiment. If the data has  different origin, e.g.
    generated at different  laboratories, please refer RP.advance.

Author(s):

    Fangxin Hong fhong@salk.edu

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:

    'topGene'   'RPadvance'   'plotRP'

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,

          RP.out <- RP(golub[,subset],golub.cl[subset],rand=123) 
          
          # class 2: label =1, class 1: label = 0
          #pfp for identifying genes that are up-regulated in class 2 
          #pfp for identifying genes that are down-regulated in class 2 
          head(RP.out$pfp)