analyseMA                package:daMA                R Documentation

_A_N_A_L_Y_S_I_S _O_F _F_A_C_T_O_R_I_A_L _M_I_C_R_O_A_R_R_A_Y _E_X_P_E_R_I_M_E_N_T_S

_D_e_s_c_r_i_p_t_i_o_n:

     analyseMA is used for the ananlysis of factorial two-colour
     microarray experiments based on the experimental design, a
     user-defined matrix containing the experimental question in
     contrast form and a vector to discern vectorial contrasts from
     contrasts given in matrix form.

_U_s_a_g_e:

     analyseMA( data, design, id, cmat, cinfo, padj=c("none","bonferroni","fdr"), tol=1e-06 ) 

_A_r_g_u_m_e_n_t_s:

    data: a matrix of size G times N containing the normalized and/or
          standardized data to be analyzed, where G is the number of
          spots under investigation and N is the number of arrays used
          in the experiment. The matrix should contain one row for each
          spot. The matrix should contain as many columns as arrays
          involved in the experiment, such that each column contains
          the data for one single array. The matrix should not contain
          any ID variables,  which are entered separately. Missing
          values should be entered as NA.

  design: the design matrix of size N times (K+2), where K is the
          number of experimental conditions. This is the design matrix
          X known from linear model theory and its elements are
          typically 0, 1, or -1. A 0 means that the associated
          parameter does not apply for the corresponding observation
          (i.e., row). The first two columns are reserved for the two
          dyes and are usually filled up with 1 and -1, respectively. 

      id: an ID vector of length G for the identification of the spots.

    cmat: a matrix describing the p experimental questions (contrasts)
          to be analysed in the experiment. The matrix can be composed
          of vectorial contrasts (a single row of the matrix) and of
          contrasts in matrix form (several rows of the matrix), e.g.
          an A times B interaction effect in a 3 times 2 design. All
          contrasts have to be combined into one matrix (using rbind
          for instance). 

   cinfo: a vector of length p describing the grouping of the contrast
          matrix rows in vector or matrix  form. E.g. if the design
          matrix contains three contrasts in vector form, cinfo =
          rep(1,3), if it contains two vectorial contratst and one as
          matrix with three rows, cinfo=c(1,1,3). 

    padj: a quoted string indicating the multiplicity adjustment that
          should be used. "none" - no multiplicity adjustment,
          "bonferroni" - Bonferroni single step adjustment, "fdr" -
          linear step-up procedure of Benjamini and Hochberg.

     tol: A value indicating the tolerance for contrast estimability
          check 

_D_e_t_a_i_l_s:

     The analysis is perfomed separately for each spot. For each spot,
     arrays with NA values are dropped. Then, for each experimental
     question (either contrast vector or contrast matrix) a check on
     the estimabilty of the resulting linear function is done. If the
     linear function of interest is estimable, t- or F-tests (whichever
     is appropriate) are computed and the associated unadjusted
     $P-$values are computed. Multiplicity adjustment is done over the
     number of spots only.

_V_a_l_u_e:

     a G times (4p+3) matrix with the following row-wise components.  

     (i): the first column contains the ID

    (ii): columns 2 though p+1 contain the estimates of the linear
          function (in case of vectorial contrasts) or the dregrees of
          freedom for the quadratic form in the numerator (in case of
          contrasts given in matrix form and that F-tests are used),
          depending on cinfo.

   (iii): columns p+2 through 2p+1 contain the test statistics (either
          t- or F-tests, depending on cinfo)

    (iv): columns 2p+2 through 3p+1 contain the raw P-values,
          associated to the t- and F-tests

     (v): column 3p+2 contains the mean square error

    (vi): column 3p+3 contains the residual degrees of freedom

   (vii): columns 3p+4 through 4p+3 contain the multiplicity adjusted
          P-values, associated to the raw P-values, as long as a
          multiplicty adjustment method has been selected

_N_o_t_e:

_A_u_t_h_o_r(_s):

     Jobst Landgrebe (jlandgr1@gwdg.de) and Frank Bretz
     (bretz@bioinf.uni-hannover.de)

_R_e_f_e_r_e_n_c_e_s:

     Bretz, F and Landgrebe J and Brunner E (2003):"Design and analysis
     of two colour factorial microarray experiments", submitted. <URL:
     http://www.microarrays.med.uni-goettingen.de/>

_S_e_e _A_l_s_o:

_E_x_a_m_p_l_e_s:

             ## Not run: 
           result <-        analyseMA( data=data.3x2, design=designs.composite$BSBSBS, id=id.3x2,
                     cmat=cmatB.AB, cinfo=c(1,3), padj=c("fdr"), tol=1e-06 ) # analyse a dataset with
                                                                             # 30012 spots and 18 arrays. The design
                                                                             # is 3x2 with 3 replicates, the
                                                                             # contrasts of interest are the main effect
                                                                             # B and the interaction effect AxB.
             
     ## End(Not run)

