generate.biomarkers: Generate discrete biomarkers from the continuous phenotypes

In pheno2geno: High-Throughput Generation of Genetic Markers and Maps from Molecular Phenotypes for Crosses Between Inbred Strains

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.

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.

Value

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

Author(s)

Konrad Zych [e-mail], Danny Arends [e-mail] Maintainer: Konrad Zych [e-mail]

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

	#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)
	
## End(Not run)

pheno2geno documentation built on May 2, 2019, 6:35 a.m.