| updateDesign {designGG} | R Documentation |
Updates current experimental design (including array.allocation and condition.allocation).
updateDesign( array.allocation, condition.allocation, nRILs,
nSlides, nEnvFactors, nTuple, btwoColorArray )
array.allocation |
matrix with nArray rows and nRIL columns. Elements of 1/0 indicate this RIL (or strain) is/not selected for this array. |
condition.allocation |
matrix with nCondition rows and nRIL columns. Elements of 1/0 indicate this RIL (or strain) is/not selected for this condition. |
nRILs |
number of RILs (or strains) available for the experiment. |
nSlides |
total number of slides available for experiment. |
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.
|
nTuple |
average number of RILs (or strains) to be assigned onto each condition. nTuple should be a real number which is larger than 1.If nTuple < 1, the algorithm will stop and show the message, warning: "The number of slides is too small to perform the experiment."
|
bTwoColorArray |
binary variable indicating experiment type: bTwoColorArray <- T #for dual channel experiment bTwoColorArray <- F #for single channel experiment
|
This function calls two subfunctions: conditionUpdate and arrayUpdate.
a list with two elements, array.allocation and condition.allocation.
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. (submitted)
http://gbic.biol.rug.nl/designGG