pheno2geno (version 1.4.0)

generate.biomarkers: Generate discrete biomarkers from the continuous phenotypes

Description

Creating genotype markers out of gene expression data.

Usage

generate.biomarkers(population, threshold=0.05, overlapInd = 10,
    proportion = c(50,50), margin = 15, pProb=0.8, n.cluster=1, env,
    verbose=FALSE, debugMode=0)

Arguments

population

An object of class population. See create.population for details.

threshold

If the pvalue for differential expression of this phenotype (see find.diff.expressed) is lower that the set threshold, the phenotype is kept in the analysis as being differentially expressed.

overlapInd

The number of individuals that are allowed in the overlap (undecided region) when assigning genotype encodings.

proportion

The expected proportion of individuals expected to carrying a certain genotype (e.g. c(50,50) in a recombinant inbred line).

pProb

Threshold posterior probability used to assign expression values to the genotypes. If not crossed - empty genotype is assigned.

n.cluster

Number of cores to be used .

env

Vector of environmental conditions - for each of the individuals specifies a condition. Ignored if missing.

margin

This specifies how much deviation from the expected proportion is allowed (2 sided).

verbose

Be verbose.

debugMode

Either use 1 or 2, this will modify the amount of information returned to the user. 1) Print out checks, 2) Print additional time information.

Value

An object of class cross. See read.cross for details

Details

This function, using the results from mixture modeling splits the continuous offspring phenotype data into discrete genotype markers, infering the direction from the founders expression data.

See Also

• read.population - Load genotype, phenotype, genetic map data files into R environment into a population object.

• cross.denovo - Create de novo genetic map or vector showing how chromosomes should be assigned.

• cross.saturate - Saturate existing map.

• find.diff.expressed - Using Rank Product or student t-test analysis to select differentially expressed genes.

Examples

# NOT RUN {
	#Example for F2 population
	set.seed(102)
	population <- fake.population(type="f2")
	population <- find.diff.expressed(population)
	population <- generate.biomarkers(population,proportion=c(25,50,25),threshold=0.01)
	
# }
# NOT RUN {
	#Example for BC population
	set.seed(102)
	population <- fake.population(type="bc")
	population <- find.diff.expressed(population)
	population <- generate.biomarkers(population,proportion=c(25,75),threshold=0.01)

	#Example for BC population
	set.seed(102)
	population <- fake.population(type="riself")
	population <- find.diff.expressed(population)
	population <- generate.biomarkers(population,proportion=c(50,50),threshold=0.01)
	
# }