experimentDesignTable: Make experiment table based design matrix
In designGG: Computational tool for designing genetical genomics experiments.

Description Usage Arguments Details Value Note Author(s) References See Also

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

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

`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.

Based on nEnvFactors and nLevels, nConditions is calculated.

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

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.

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>

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

designGG documentation built on May 2, 2019, 5:51 a.m.