interactionLevel {designGG}R Documentation

Generate levels for all interacting factors

Description

Generate levels for all interacting factors for all RILs (or strains). This is a subfunction needed for designScore, but is not directly used.

Usage

  interactionLevel( genotype.level, condition.level, markerIndex, 
                    nEnvFactors )

Arguments

genotype.level

levels of genetic factor for each RIL (or strain) in the experiment.

condition.level

levels of all environmental factors for each RIL (or strain)in the experiment.

markerIndex

indicate which genome position that level of genetic factor corresponds to.

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.

Details

markerIndex indicates the genome position that genotype.level corresponds to.
An experiment design is defined to be optimal over all markers if the sum of scores, e.g. A-optimality criterion over all markers is minimized.

Value

a matrix with nRILs rows. The number columns depends on nEnvFactors. For example:
If nEnvFactors = 1, there is only one interaction term.
If nEnvFactors = 2, there are three pair-wise two-way interaction terms and one three-way interaction term.

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

See Also

designScore, conditionLevel


[Package designGG version 1.1 Index]