utilities              package:EBarrays              R Documentation

_U_t_i_l_i_t_y _f_u_n_c_t_i_o_n_s _f_o_r _t_h_e _E_B_a_r_r_a_y_s _p_a_c_k_a_g_e

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

     Utilitiy functions for the EBarrays package

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

     ebPatterns(x, ordered=FALSE)

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

       x: x can be a character vector (of length > 2) (see example), or
          an arbitrary connection which should provide patterns, one
          line for each pattern. If 'x' is a character vector of length
          1, it is assumed to be the name of a file (since there's no
          point in a patterns object with only one pattern) which is
          then opened and treated as a connection. 

 ordered: logical variable specifying whether the pattern is ordered or
          not

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

     'ebPatterns' creates objects that represent a collection of
     hypotheses to be used by 'emfit'.

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

     'ebPatterns' creates an Object of class ``ebarraysPatterns'', to
     be used in other functions such as 'emfit'. This is nothing more
     than a list (and can be treated as such as far as indexing goes)
     and is used only for method dispatch.

_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:

     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"), TRUE)
     show(patterns)

