conditionLevel: Levels of all environmental factors

In designGG: Computational tool for designing genetical genomics experiments.

Description

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.

Usage

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

Arguments

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.

Details

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.

Value

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.

Author(s)

Yang Li <[email protected]>, Gonzalo Vera <[email protected]>
Rainer Breitling <[email protected]>, Ritsert Jansen <[email protected]>

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

See Also

designScore, conditionCombination

designGG documentation built on May 29, 2017, 6:42 p.m.