experimentDesignTable        package:designGG        R Documentation

_M_a_k_e _e_x_p_e_r_i_m_e_n_t _t_a_b_l_e _b_a_s_e_d _d_e_s_i_g_n _m_a_t_r_i_x

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

     This function generates two '.csv' files which descibe how samples
     are allocated samples into different conditions and paired on
     arrays.

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

       experimentDesignTable( array.allocation, condition.allocation, 
                              nEnvFactors, nLevels, Level, fileName,envFactorNames,
                              directory )

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

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

condition.allocation: matrix with nCondition rows and nRIL columns.
          Elements of 1/0 indicate this RIL 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 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 facotor). 

fileName: the final optimal design table(s) in 'csv' format and a plot
          (in 'png'  format) of the all scores during SA process (if
          'plotScores' = T) will be  produced. The users can specify
          the table and plot name by setting  'fileName'. If 'NULL'
          (default) it produces files starting with '"myDesignGG"'. 

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. 

directory: It tells where the resulting optimal design tables are to be
          stored. If 'NULL' (default), it will use the currect working
          directory. 

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

     Based on 'nEnvFactors' and 'nLevels', 'nConditions' is calculated.

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

     Two tables report the results: table "pair design" which is only
     used for  two-channel experiments and describes how samples are
     paired together on the  slide (e.g. microarray chip), and table
     "envi-ronment design" which is used  when there are more
     environments evolved in the experiment. With these two  tables,
     the experimenters can set up the environmental treatment and 
     follow-up profiling measurement.  
      Examples: 
      1. conditionDesign.csv 

                   Temperature  Cell Type                Selected Samples           
       condition1      15           A      RIL28  RIL81       RIL18        RIL61    
       condition2      24           A      RIL72  RIL40       RIL83        RIL44  RIL10
       condition3      29           A      RIL22  RIL89        RIL3        RIL30  RIL58
       condition4      15           B      RIL70  RIL47        RIL4        RIL59    
       condition5      24           B      RIL93  RIL97       RIL49        RIL14    

     2. arrayDesign.csv  

               Channel 1  Channel 2
       array1      RIL28      RIL92
       array2      RIL70      RIL47
       array3      RIL22      RIL89
       array4      RIL45      RIL15
       array5      RIL52      RIL41

_N_o_t_e:

     The optimal design results are described in two tables. One is
     called "array design" which is only used for two-channel
     experiments. It describes how  samples are paired together on the
     slide (e.g. microarray chip). The other  table is  called
     "condition design" which is used when there is more than one
     environmental factor involved in the experiment. Each cell in
     condition  design table represents a combination of different
     levels of environmental factors and the selected sample names
     (e.g. RIL names) for this condition are shown. Based on these two
     tables, the experimenters can set up the  environmental treatment
     and follow-up profiling measurement.

_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, R. Breitling and R.C. Jansen. Generalizing genetical
     genomics: the added value from environmental perturbation, Trends
     Genet (2008) 24:518-524. 
      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

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

     'designGG', 'exampleArrayDesignTable',
     'exampleConditionDesignTable'

