AffyRegress           package:AffyExpress           R Documentation

_S_e_l_e_c_t _d_i_f_f_e_r_e_n_t_i_a_l_l_y _e_x_p_r_e_s_s_e_d _g_e_n_e_s, _a_n_d _o_u_t_p_u_t _t_h_e _r_e_s_u_l_t _t_o _a _h_t_m_l _f_i_l_e

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

     This is a wrapper function complete the following tasks: 1. 
     Create a design matrix 2.  Create a contrast  3.  Run regression
     4.  Select differentaly expressed gene 5.  Output the
     differentally expressed gene to a html file

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

     AffyRegress(normal.data, cov, compare1, compare2, method, 
         int=NULL, level=NULL, adj="none", p.value=0.05, m.value=0, 
             filename="result")

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

normal.data: an 'ExpressionSet'

     cov: a list of 1-n covariates

compare1: the first value of the main covariate.  For example, suppose
          that the main covariate is drug, and there are three unique
          values: "drug1", "drug2", and "placebo".  You would like to
          compare "drug1" to "drug2".  Then you would use "drug1" as
          compare1

compare2: the second value of the main covariate. Based on the
          previoius example, if you would like to compare "drug1" vs
          "drug2", then you would use "drug2" as compare2

  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

     int: if int=NULL, the interaction effect is not considered;
          otherwise, use two integers to indicate which covariates are 
          considered for interaction effect.  For example,  if
          cov<-c("estrogen","drug","time") and you are considering the
          interaction between "estrogen" and  "time", then you would
          write    int=c(1,3) 

   level: you only specify this term when the design matrix contains an
          interaction term.  Suppose that you would like to compare
          "drug1" to "drug2" only when estrogen is "present", where
          "present" is one of the values of the estrogen variable.  You
          will use "present" as level.

     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.

 p.value: p value, the default value is 0.05

 m.value: fold change cut-off value, default value is 0

filename: name of the output file

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

     A dataframe which has the same format as the one created by
     select.sig.gene  function.

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

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

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

     data(testData)
     normaldata<-pre.process("rma",testData)
     result<-AffyRegress(normaldata, "group", "A", "C", "L")

