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

     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 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

_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)
       

