regress             package:AffyExpress             R Documentation

_R_u_n _r_e_g_r_e_s_s_i_o_n _t_o _f_i_t _g_e_n_e_w_i_s_e _l_i_n_e_a_r _m_o_d_e_l

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

     Fit genewise linear model using LIMMA package, ordinary linear
     regression,  or permutation method.

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

     regress(object, design, contrast, method, adj="none", permute.time=1000) 

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

  object: an "ExpressionSet"

  design: design matrix from the make.design function

contrast: contrast matrix from the make.contrast function

  method: Three methods are supported by this function: "L" for using
          LIMMA method - compute moderated t-statistics and log-odds 
          of differential expression by empirical Bayes shrinkage of
          the standard  errors towards a common value,   "F" for using
          ordinary linear regression,  "P" for permuation test by
          resampling the phenotype

     adj: adjustment method for multiple comparison test, including
          "holm",  "hochberg", "hommel", "bonferroni", "BH", "BY",
          "fdr", "none".  The default value is "none". Type
          help(p.adjust) for more detail.

permute.time: number of permutation times, only used for the
          permutation  method.

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

     A dataframe contains rows for all the genes from object and the
     following columns: ID(probeid); Log2Ratio (estimate of the effect
     or the contrast,  on the log2 scale);  F (F statistics); P.Value
     (raw p-value); adj.P.Value (adjusted  p-value or q-value)

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

     Xiwei Wu xwu@coh.org, Xuejun Arthur Li xueli@coh.org

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

     Smyth, G.K. (2005) Limma: linear models for microarray data. In:
     Bioinformatics and Computational Biology Solutions using R and 
     Bioconductor, R. Gentleman,  V. Carey, S. Dudoit, R. Irizarry,  W.
     Huber (eds.), Springer, New York, pages 397-420

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

     data(testData)
     normaldata<-pre.process("rma",testData)

     ## Create design matrix
     design<-make.design(pData(normaldata), "group")

     ## Create contrast matrix - Compare group "A" vs. "C"
     contrast<-make.contrast(design, "A", "C")

     ## Identify differentially expressed gene by using LIMMA method
     result<-regress(normaldata, design, contrast, "L")

