designScore             package:designGG             R Documentation

_C_a_l_c_u_l_a_t_e _t_h_e _A- _o_r _D- _o_p_t_i_m_a_l_i_t_y _s_c_o_r_e _b_a_s_e_d _o_n _c_u_r_r_e_n_t _e_x_p_e_r_i_m_e_n_t_a_l _d_e_s_i_g_n

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

     According to the current experimental design, the Fisher
     information matrix  is obtained and then either the A- or D-
     optimality score is computed.

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

       designScore( genotype, array.allocation, condition.allocation, 
                    nEnvFactors, nLevels, Level, nConditions, weight=1, 
                    optimality="A", bTwoColorArray, envFactorNames)

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

genotype: genotype data: a nMarker-by-nRILs matrix with two allels
          being 0 and 1 (or A and B) or three allels being 0, 0.5 and 1
          (or, A, H, and B), where 0.5 (or H) represents heterozygous
          allele. 

array.allocation: matrix with nArray rows and nRIL columns. Elements of
          1/0 indicate this RIL (or strains) is/not selected  for this
          array.  

condition.allocation: matrix with nCondition rows and nRIL columns.
          Elements of 1/0 indicate this RIL (or strains) is/not
          selected  for this condition.  

nEnvFactors: number of environmental factors, an integer bewteen 1 and
          3. When 'nEnvFactors' is 1 and the number of levels for the
          enviromental factor  ('nLevels')is 1, there is one condition
          in the experiment (i.e. no  enviromental perturbation) and
          thus only genetic factor will be considered in the algorithm.
          When 'nEnvFactors' is 1 and nLevels is larger than 1 or
          'nEnvFactors' is larger than 1, all main factor(s) and
          interacting facotr(s) will be included. Examples: If there is
          a temperature perturbation, then 'nEnvFactors' is 1; If there
          is both temperature and drug treatment perturbation,  then
          'nEnvFactors' is 2. 

 nLevels: number of levels for each factor, a vector with each
          component being an integer. The length of it should equal
          'nEnvFactors'. 

   Level: a list which specifies the levels for each factor in the
          experiment. There are in total 'nEnvFactors' elements in the
          list and each element correpsond to certain envrironmental
          factor. The emlemet is a vector describing all levels of the
          environmental factor. default setting for the level of each
          factor is 1, 2, ... nLevels[i]. (Here nLevels[i] is the _i_th
          element of nLevels, which gives the total number of levels
          for _i_ environmental factor). 

nConditions: number of all possible combination of all environmental
          factors.   

  weight: a vector with length of variableNumber which is calculated
          from function 'variableNumber'. Default = 1 (which means the
          parameters to be  estimated are equally important during
          optimization.) 

optimality: type of optimality, i.e. "A" (A-optimality) or "D"
          (D-optimality). A-optimality minimizes $Trace((X'X)^{-1})$,
          which corresponds to minimum average variance of the
          parameter estimates. D-optimality minimizes $det(X'X)^{-1}$,
          which corresponds to minimum generalized variance of the
          parameter estimates. 

bTwoColorArray: binary variable indicating experiment type: 
           'bTwoColorArray <- T' #for dual channel experiment 
           'bTwoColorArray <- F' #for single channel experiment  

envFactorNames: a vector with names for all environmental factor(s).
          For example, for the experiment with two environmental
          factors of temperature and drug treatment: 'envFactorNames <-
          c( "Temperature", "Dosage" ) '
           Default = 'NULL', then the output will use "F1" and "F2" to
          indicate the environmental factors. 

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

     Example parameter settings:
      Suppose to design an experiment with two environmental factors
     (F1, F2) and  there are two diffferent levels for each
     environment. The levels are 16 and 24 for F1, and 5 anf 10 for F2.
     Thus the following command can be used:
      'nEnvFactors <- 2'
      'nLevels <- c ( 2, 2 )'
      'levels <- list ( c(16, 24), c(5, 10) )'
      The length of parameter 'weight' is dependent on the number of
     environmental factors: 
      When 'nEnvFactor' = 0, 
      'weight' is 1 as there is only one parameter of interest
     (genotype).
      When 'nEnvFactor' = 1, 
       'weight' = c( $w_{Q}$, $w_{F1}$, $w_{QF1}$ ) 
      When 'nEnvFactor' = 2, 
       'weight' = c( $w_{Q}$, $w_{F1}$, $w_{F2}$, $w_{QF1}$, $w_{QF2}$,
     $w_{F1F2}$, $w_{QF1F2}$) 
      When 'nEnvFactor' = 3, 
      'weight' = c( $w_{Q}$, $w_{F1}$, $w_{F2}$, $w_{F2}$, $w_{QF1}$,
     $w_{QF2}$, $w_{QF3}$, $w_{F1F2}$, $w_{F1F3}$, $w_{F2F3}$,
     $w_{QF1F2}$, $w_{QF1F3}$, $w_{QF2F3}$, $w_{QF1F2F3}$ ) 
          Here $w_{Q}$ represents the weight for genotype effect, 
     $w_{F1}$ represent the  weight for F1 effect and $w_{QF1}$
     represent the weight for interaction between genotype and F1
     effect, etc.

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

     The score is defined as the "double" sum of the variances, summed
     over all  parameters and over all markers.

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

     Yang Li <yang.li@rug.nl>, Gonzalo Vera
     <gonzalo.vera.rodriguez@gmail.com> 
      Rainer Breitling <r.breitling@rug.nl>, Ritsert Jansen
     <r.c.jansen@rug.nl>

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

     Y. Li, M. Swertz, G. Vera, J. Fu, R. Breitling, and R.C. Jansen.
     designGG:  An R-package and Web tool for the optimal design of
     genetical genomics  experiments. (submitted) 
      http://gbic.biol.rug.nl/designGG 
      Y. Li, R. Breitling and R.C. Jansen. Generalizing genetical 
     genomics: the added value from environmental perturbation, Trends
     Genet (2008) 24:518-524. 
      E. Wit and J. McClure. Statistics for Microarrays: Design,
     Analysis and Inference. (2004) Chichester: Wiley.

_S_e_e _A_l_s_o:

     'designGG'

