betr                  package:betr                  R Documentation

_B_a_y_e_s_i_a_n _E_s_t_i_m_a_t_i_o_n _o_f _T_e_m_p_o_r_a_l _R_e_g_u_l_a_t_i_o_n (_B_E_T_R): _C_a_l_c_u_l_a_t_e _t_h_e _p_r_o_b_a_b_i_l_i_t_y _o_f _d_i_f_f_e_r_e_n_t_i_a_l _e_x_p_r_e_s_s_i_o_n _i_n _t_i_m_e-_c_o_u_r_s_e _d_a_t_a

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

     Calculate the probability of differential expression of each
     feature in a microarray gene expression time-course data set.

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

     betr(eset, cond=NULL, timepoint, replicate, twoColor = FALSE, twoCondition = NULL, alpha = 0.05, verbose=FALSE)

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

    eset: object of class matrix, ExpressionSet or exprSet containing
          log-ratios or log-values of expression for a series of
          microarrays

    cond: character or factor vector giving the experimental group for
          each sample of eset. Not required for a single-condition
          time-course.

timepoint: numeric vector giving the time point for each sample of eset

replicate: character or factor vector giving the replicate ID of each
          sample of eset

twoColor: boolean indicating whether the data is from a two-color
          microarray platform

twoCondition: boolean indicating whether the data is from a two
          condition experiment (as opposed to a single condition
          experiment where the comparison is between baseline and
          subsequent time points)

   alpha: the desired False Discovery Rate

 verbose: whether to output more detailed information about the model
          fitting

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

     This function fits a model to estimate the probability of
     differential for each feature of time-course data set.

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

     a numeric vector of the probability of differential expression for
     each feature in the data set.

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

     Martin Aryee

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

     The algorithm is described in detail in: (..undergoing review..)

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

     library(Biobase)    
     data(timeEset)
     prob <- betr(timeEset, cond=pData(timeEset)$strain, 
         timepoint=pData(timeEset)$time, replicate=pData(timeEset)$rep, alpha=0.05)
     head(prob)

