bcomb                  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 signal for each condition
     from replicates  using Bayesian models. The inputs are gene
     expression levels and the probe-level standard deviation
     associated with expression measurement for each gene on each chip.
     The outputs include gene expression levels and standard deviation
     for each condition. This function was originally part of the
     'pplr' package. Although this function can be called directly, it
     is recommended to use the 'pumaComb' function instead, which can
     work directly on 'ExpressionSet' objects, and can automatically
     determine which arrays are replicates.

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

     bcomb(e, se, replicates, method=c("map","em"), 
           gsnorm=FALSE, nsample=1000, eps=1.0e-6)

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

       e: a data frame containing the expression level for each gene on
          each chip. 

      se: a data frame containing the standard deviation of gene
          expression levels. 

replicates: a vector indicating which chip belongs to which condition. 

  method: character specifying the method algorithm used.

  gsnorm: logical specifying whether do global scaling normalisation or
          not. 

 nsample: integer. The number of sampling in parameter estimation. 

     eps: a numeric, optimisation parameter. 

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

     Each element in replicate represents the condition of the chip
     which is in the same column order as in the expression and
     standard deviation matrix files.

     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
     Bayesion model as in method "map" but with conjugate prior on
     between-replicate variance and shares the variance across
     conditions.

     The parameter nsample should be large enough to ensure stable
     parameter estimates. Should be at least 1000.

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

     The result is a data frame with components named 'M1', 'M2', and
     so on, which represent the mean expression values for condition 1,
     condition 2, and so on. It also has components named 'Std1',
     'Std2', and so on, which represent the standard deviation of the
     gene expression values for condition 1, condtion 2, and so on.

_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 'pumaComb', 'mmgmos' and 'pplr'

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

       data(exampleE)
       data(exampleStd)
       r<-bcomb(exampleE,exampleStd,replicates=c(1,1,1,2,2,2),method="map")

