RP                 package:RankProd                 R Documentation

_R_a_n_k _P_r_o_d_u_c_t _A_n_a_l_y_s_i_s _o_f _M_i_c_r_o_a_r_r_a_y

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

    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.

_V_a_l_u_e:

     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. 

_N_o_t_e:

     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.

_A_u_t_h_o_r(_s):

     Fangxin Hong fhong@salk.edu

_R_e_f_e_r_e_n_c_e_s:

     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

_S_e_e _A_l_s_o:

     'topGene'   'RPadvance'   'plotRP'

_E_x_a_m_p_l_e_s:

          
           # 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)
       

