postprob              package:EBarrays              R Documentation

_C_a_l_c_u_l_a_t_e_s _p_o_s_t_e_r_i_o_r _p_r_o_b_a_b_i_l_i_t_i_e_s _f_o_r _e_x_p_r_e_s_s_i_o_n _p_a_t_t_e_r_n_s

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

     Takes the output from emfit and calculates the posterior
     probability of each of the hypotheses, for each gene.

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

     postprob(fit, data, ...)

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

     fit: output from 'emfit'

    data: a numeric matrix or an object of class ``ExpressionSet''
          containing the data, typically the same one used in the
          'emfit' fit supplied below. 

     ...: other arguments, ignored

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

     An object of class ``ebarraysPostProb''. Slot 'joint' is an three
     dimensional array of probabilities. Each element gives the
     posterior probability that a gene belongs to certain cluster and
     have certain pattern. 'cluster' is a matrix of probabilities with 
     number of rows given by the number of genes in 'data' and as many
     columns as the number of clusters for the fit. 'pattern' is a
     matrix of probabilities with  number of rows given by the number
     of genes in 'data' and as many columns as the number of patterns
     for the fit. It additionally contains a slot `hypotheses'
     containing these hypotheses.

_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:

     'emfit'

_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)
     prob <- postprob(gg.fit,eset)

