initialDesign: Initialize an experiment design matrix

In designGG: Computational tool for designing genetical genomics experiments.

Description

Allocate RILs (or strains) into different conditional and pair RILs (or strains) into slides.

Usage

  initialDesign( genotype, nRILs, nSlides, nConditions, nTuple, 
                 bTwoColorArray )

Arguments

genotype	genotype data: a nMarker-by-nRILs matrix with two allels being 0 and 1 (or A and B) or three allels being 0, 0.5 and 1 (or, A, H, and B), where 0.5 (or H) represents heterozygous allele.
nRILs	total number of RILs ((or strains) available for the experiment.
nSlides	total number of slides available for the experiment.
nConditions	number of all possible combination of all environmental factors.
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 shw the message below, warning: "The number of slides is too small to perform the experiment."
bTwoColorArray	binary variable indicating experiment type: bTwoColorArray <- TRUE \#for dual channel experiment bTwoColorArray <- FALSE \#for single channel experiment

Details

For two-color array experiments, randomly choose a RIL (or strain) and pair it with the genetically most different RIL (or strain) on one array.
For one-color array experiments, array.allocation is NULL as there is no need to pair samples.

Value

a list with 2 matrices:
condition.allocation: allocate RILs (or strains) into different conditional (nCondition * nRILs)
array.allocation: pair RILs (or strains) into sldies (nSlides * nRILs)

Note

This function calls conditionAllocation function to allocate selected RILs (or strains) into different conditions.

Author(s)

Yang Li <[e-mail]>, Gonzalo Vera <[e-mail]>
Rainer Breitling <[e-mail]>, Ritsert Jansen <[e-mail]>

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

designGG

Examples

library(designGG)
data(genotype)
nRILs <-100
nEnvFactors <- 2  
nConditions <-2
nLevels <- c( 2, 2 )
levels <- list ( c(16, 24), c(5, 10) )
nSlides <- 100
nTuple <- 25  
bTwoColorArray <- TRUE 
initialDesign( genotype, nRILs, nSlides, nConditions, nTuple, bTwoColorArray )          

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