emfit                package:EBarrays                R Documentation

_I_m_p_l_e_m_e_n_t_s _E_M _a_l_g_o_r_i_t_h_m _f_o_r _g_e_n_e _e_x_p_r_e_s_s_i_o_n _m_i_x_t_u_r_e _m_o_d_e_l

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

     Implements the EM algorithm for gene expression mixture model

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

     emfit(data,
           family,
           hypotheses,
           cluster,
           type=2,
           criterion="BIC",
           cluster.init = NULL,
           num.iter = 20,
           verbose = getOption("verbose"),
           optim.control = list(),...)

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

    data: a matrix

  family: an object of class ``ebarraysFamily'' or a character string
          which can be coerced to one. Currently, only the characters
          "GG" and "LNN", and "LNNMV" are valid. For LNNMV, a 'groupid'
          is required. See below. Other families can be supplied by
          constructing them explicitly. 

hypotheses: an object of class ``ebarraysPatterns'' representing the
          hypotheses of interest. Such patterns can be generated by the
          function 'ebPatterns' 

 cluster: if 'type'=1, 'cluster' is a vector specifying the fixed
          cluster membership for each gene; if 'type'=2, 'cluster'
          specifies the number of clusters to be fitted

    type: if 'type'=1, the cluster membership is fixed as input
          'cluster'; if 'type'=2, fit the data with a fixed number of
          clusters

criterion: only used when 'type'=2 and 'cluster' contains more than one
          integers. All numbers of clusters provided in 'cluster' will
          be fitted and the one that minimizes 'criterion' will be
          returned. Possible values now are "BIC", "AIC" and "HQ"

cluster.init: only used when 'type'=2. Specify the initial clustering
          membership.

num.iter: number of EM iterations

 verbose: logical or numeric (0,1,2) indicating desired level of
          information printed for the user

optim.control: list passed unchanged to 'optim' for finer control

     ...: 'groupid': an integer vector indicating which group each
          sample belongs to, required  in the ``LNNMV'' model. It does
          not depend on ``hypotheses''.

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

     an object of class ``ebarraysEMfit'', that can be summarized by
     'show()' and used to generate posterior probabilities using
     'postprob'

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

     Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and
     Christina Kendziorski

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

     Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R.
     (2001). On differential variability of expression ratios:
     Improving statistical inference about gene expression changes from
     microarray data. Journal of Computational Biology 8:37-52.

     Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On
     parametric empirical Bayes methods for comparing multiple groups
     using replicated gene expression profiles. Statistics in Medicine
     22:3899-3914.

     Newton, M.A. and Kendziorski, C.M. Parametric Empirical Bayes
     Methods for Microarrays in The analysis of gene expression data:
     methods and software. Eds. G. Parmigiani, E.S. Garrett, R.
     Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.

     Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004).
     Detecting differential gene expression with a semiparametric
     hierarchical mixture model. Biostatistics 5: 155-176.

     Yuan, M. and Kendziorski, C. (2006). A unified approach for
     simultaneous gene clustering and differential expression
     identification. Biometrics 62(4): 1089-1098.

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

     'ebPatterns', 'ebarraysFamily-class'

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

     data(sample.ExpressionSet) ## from Biobase
     eset <- exprs(sample.ExpressionSet)
     patterns <- ebPatterns(c("1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
                              "1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2"))
     gg.fit <- emfit(data = eset, family = "GG", hypotheses = patterns, verbose = TRUE)
     show(gg.fit)

