pumaComb                package:puma                R Documentation

_C_o_m_b_i_n_i_n_g _r_e_p_l_i_c_a_t_e_s _f_o_r _e_a_c_h _c_o_n_d_i_t_i_o_n

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

     This function calculates the combined (from replicates) signal for
     each condition using Bayesian models. The inputs are gene
     expression levels and the probe-level standard deviations
     associated with expression measurements for each gene on each
     chip. The outputs include gene expression levels and standard
     deviation for each condition.

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

     pumaComb(
             eset
     ,       design.matrix=NULL
     ,       method="em"
     ,       numOfChunks=1000
     ,       save_r=FALSE
     ,       cl=NULL
     ,       parallelCompute=if(
                     "Rmpi" %in% installed.packages() & "snow" %in% installed.packages()
             )
             as.logical(length(grep("origin",system("lamnodes",TRUE,TRUE))))
             else FALSE
     )

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

    eset: An object of class 'ExpressionSet'. 

design.matrix: A design matrix. 

  method: Method "map" uses MAP of a hierarchical Bayesion model with
          Gamma prior on the between-replicate variance (Gelman et.al.
          p.285) and shares the same variance across conditions. This
          method is fast and suitable for the case where there are many
          conditions.

          Method "em" uses variational inference of the same
          hierarchical Bayesian model as in method "map" but with
          conjugate prior on between-replicate variance and shares the
          variance across conditions. This is generaly much slower than
          "map", but is recommended where there are few conditions (as
          is usually the case). 

numOfChunks: An integer defining how many chunks the data is divided
          into before processing. There is generally no need to change
          the default value. 

  save_r: Will save an internal variable 'r' to a file. Used for
          debugging purposes. 

      cl: A "cluster" object. See 'makeCluster' function from 'snow'
          package for more details (if available).

parallelCompute: Boolean identifying whether processing in parallel
          should occur. 

_D_e_t_a_i_l_s:

     It is generally recommended that data is normalised prior to using
     this function. See the function 'pumaNormalize'.

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

     The result is an 'ExpressionSet' object.

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

     Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray

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

     Gelman,A., Carlin,J.B., Stern,H.S., Rubin,D.B., Bayesian data
     analysis. London: Chapman & Hall; 1995.

     Liu,X., Milo,M., Lawrence,N.D. and Rattray,M. (2005) Probe-level
     variances improve accuracy in detecting differential gene
     expression, technical report available upon request.

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

     Related methods 'pumaNormalize', 'bcomb', 'mmgmos' and 'pumaDE'

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

             library(puma)
             data(affybatch.example)
             pData(affybatch.example) <- data.frame("level"=c("twenty","twenty","ten")
                 , "batch"=c("A","B","A"), row.names=rownames(pData(affybatch.example)))
             eset_mmgmos <- mmgmos(affybatch.example)
             pData(eset_mmgmos)
             exprs(eset_mmgmos)[1:3,]
             eset_comb <- pumaComb(eset_mmgmos)
             pData(eset_comb)
             exprs(eset_comb)[1:3,]

