conditionLevel: Levels of all environmental factors
In designGG: Computational tool for designing genetical genomics experiments.

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

Describe the levels of all environmental factors for each RIL/strain in the experiment.
This is a subfunction needed for designScore, but is not directly used.

1 2	conditionLevel( array.allocation, condition.allocation, condition.combination,nEnvFactors )

`array.allocation`	a matrix with nArray rows and nRIL columns. Elements of 1/0 indicates this RIL (or strain) is/not selected for this array.
`condition.allocation`	a matrix with nCondition rows and nRIL columns. Elements of 1/0 indicates this RIL (or strain) is/not selected for this condition.
`condition.combination`	a matrix indicating all possible levels for environmental factors, with dimension of nConditions by nEnvFactors.
`nEnvFactors`	number of environmental factors, an integer bewteen 1 and 3. When `nEnvFactors` is 1 and 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.

For single-channel experiment, array.allocation is NULL. Then the conditionLevel is decided by condition.allocation. For dual-channel experiment, array.allocation decides which RILs are selected and then the condition.allocation indicates which condition this RIL will be put in for the experiment.

A matrix with dimension of nRILs by nEnvFactors, each element indicates the level of a certain environmental factor to which the RIL (or strain) is exposed in the experiment.

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

designScore, conditionCombination

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