designMA                package:daMA                R Documentation

_D_E_S_I_G_N _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:

     designMA computes efficient factorial microarray experimental
     designs for two-colour microarrays based on a list of user-defined
     design matrices, a matrix describing the experimental questions
     (contrasts), a vector to discern vectorial contrasts from
     contrasts given in matrix form and a design optimality criterion.

_U_s_a_g_e:

     designMA(design.list, cmat, cinfo, type = c("d", "e", "t"), tol = 1e-06)

_A_r_g_u_m_e_n_t_s:

design.list: a named list of design matrices. Each design matrix should
          have nrow = number of arrays and ncol= number of experimental
          conditions. With p columns, the first two columns describe
          the dye labeling (green and red), the remaining columns
          describe the experimental  conditions. 

    cmat: a matrix describing the 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 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). 

    type: a quoted letter indicating the optimality criterion that
          shoul be used. "d" - determinant, "e" - eigenvalue, "t" -
          trace.

     tol: A value indicating the tolerance for contrast estimability
          check.

_D_e_t_a_i_l_s:

     The choice of the optimality criterion influences the design
     defined as best. We propose the trace criterion because of its
     straightforward interpretability. For a detailed description of
     optimality criteria cf. Pukelsheim, F. "Optimal Design of
     Experiments", New York 1993.

_V_a_l_u_e:

     a list with the following components 

  alleff: a matrix giving the absolute efficiency values (cols) for
          each contrast (rows). NA if contrast is not estimatable. 

alleffrel: a matrix giving the relative efficiency values (cols) for
          each contrast (rows). The values are obtained by dividing the
          absolute values by the by the maximal efficiency value for a
          given contrast. NA if contrast is not estimatable. 

alleffave: a vector giving the average efficiency for each design  over
          all contrasts.

effdesign: the name of the design with the highest alleffave value.

      df: a vector with the degrees of freedom of the F-statistics
          obtained by the designs.

_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: designs.basic # look at typical basic designs
                     ## Not run: designs.composite  #look at comlpex composite designs
                     ## Not run: 
     t.eff.3x2.B.AB <-  designMA(designs.composite,
                                                 cmatB.AB,cinfoB.AB,type="t")# compute design efficiencies for
                                                                             # a \eqn{3 \times 2} factorial experiment
                                                                             # using 18 microarrays and asking for 
                                                                             # the main effect B and the interaction effect \eqn{A \times B}
                     
     ## End(Not run)
                     ## Not run: 
     t.eff.3x2.all <-  designMA(designs.composite,
                                                         cmat,cinfo,type="t")
                      
     ## End(Not run)                                                      #compute design efficiencies design for
                                                                             # a \eqn{3 \times 2} factorial
                                                                             # experiment using 18
                                                                             # microarrays and asking for 
                                                                             # the the simple B
                                                                             # effects, the main effects
                                                                             # A, B and the interaction
                                                                             # effect \eqn{A  \times B}

