RPadvance              package:RankProd              R Documentation

_A_d_v_a_n_c_e_d _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:

     Advance rank product method to identify  differentially expressed
     genes. It is possible to combine data from different studies, e.g.
     data sets  generated at different laboratories.

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

         RPadvance(data,cl,origin,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 group
          data, the label for  each sample should be 1.

  origin: a vector containing the origin labels of the  sample. e.g.
          for  the data sets generated at multiple laboratories, the
          label is the same for samples within one lab and different
          for samples  from different labs. 

num.perm: number of permutations used in the calculation  of the null
          density. Default is 'B=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 genewise mean of the non-missing values. Gene
          will all value missing  will be assigned "NA"

gene.names: if "NULL", no gene name will be attached  to the estimated
          percentage of false prediction (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.

_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 i dentificaiton
          each 

  RPrank: rank of the rank products of each gene in  ascending order

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

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

  all.FC: fold change of class 1/class 2 under each origin. log-fold
          change if data is in log scaled

_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.   

     The function is able to replace function RP in the  same library.
     it is a more  general version, as it is able to handle data from
     differnt origins.

_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'   'RP'   'plotRP'  'RSadvance'

_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)

           ##For data with single origin
           subset <- c(1:4,28:30)
           origin <- rep(1,7)
           #identify genes 
           RP.out <- RPadvance(golub[,subset],golub.cl[subset],
                                origin,plot=FALSE,rand=123)
           
           #For data from multiple origins
           
           #Load the data arab in the package, which contains 
           # the expression of 22,081 genes
           # of control and treatment group from the experiments 
           #indenpently conducted at two 
           #laboratories.
           data(arab)
           arab.origin #1 1 1 1 1 1 2 2 2 2
           arab.cl #0 0 0 1 1 1 0 0 1 1
           RP.adv.out <- RPadvance(arab,arab.cl,arab.origin,
                         num.perm=100,gene.names=arab.gnames,logged=TRUE,rand=123)

           attributes(RP.adv.out)
           head(RP.adv.out$pfp)
           head(RP.adv.out$RPs)
           head(RP.adv.out$AveFC)

