experimentDesignTable {designGG} R Documentation

## Make experiment table based design matrix

### Description

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

### Usage

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


### Arguments

 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 ith 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.

### Details

Based on nEnvFactors and nLevels, nConditions is calculated.

### Value

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

### Note

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.

### Author(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>

### References

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. BMC Bioinformatics 10:188(2009)
http://gbic.biol.rug.nl/designGG

designGG, exampleArrayDesignTable, exampleConditionDesignTable