simCNVdataCaseCon: Simulation of CNV in a case-control study design

In isglobal-brge/CNVassoc: Association analysis of CNVs and imputed SNPs incorporating uncertainty

Description

This function simulates intensity for a CNV within cases and control groups for different scenarios

Usage

simCNVdataCaseCon(n0, n1, w0, or, mu.surrog0, sd.surrog0, 
            mu.surrog1 = mu.surrog0, sd.surrog1 = sd.surrog0, random = TRUE)

Arguments

n0	number of controls simulated
n1	number of cases simulated
w0	vector of proportions of copy number status in controls
or	a vector of odds ratio for one, two,... copies respect to zero copies
mu.surrog0	vector of means of CNV intensity signal, per copy number status, in control group
sd.surrog0	vector of standard deviations of CNV intensity signal, per copy number status, in control group
mu.surrog1	vector of means of CNV intensity signal, per copy number status, in control group
sd.surrog1	vector of standard deviations of CNV intensity signal, per copy number status, in control group
random	A logical value. TRUE means that individuals (rows) are randomly permuted, and FALSE means that simulated 'data.frame' contains controls first and then cases. Default value is TRUE

Details

This function is useful to calculate the power of association models in a case control study design under different scenarios ,e.g. setting different degrees of association (odds ratios), considering different degrees of uncertainty controlled by the distribution of intensity signal data, i.e. mean mu.surrog, standard deviation sd.surrog and proportion w, etc.

Value

Data frame with individual simulated data per row and with the following variables:

resp	Trait (response) variable with 0 or 1 if the individual is a control or a case respectively
surrog	Signal intensity following a mixture of normals with means, standard deviations and proportions specified by mu.surrog, sd.surrog and w respectively, within cases and controls
cnv	True copy number status

See Also

simCNVdataBinary, simCNVdataNorm, simCNVdataPois, simCNVdataWeibull, cnv, CNVassoc

Examples

maf<-0.3
set.seed(123)
simData<-simCNVdataCaseCon(n0=1000, n1=1000, mu.surrog0=c(0,0.5,1), 
        sd.surrog0=rep(0.15,3), mu.surrog1=c(0,0.5,1), sd.surrog1=rep(0.15,3), 
        w0=c((1-maf)^2,2*maf*(1-maf), maf^2), or=c(1.3,1.3^2),  
        random = FALSE)
CNV<-cnv(simData$surrog,mix.method="EMmixt")
getQualityScore(CNV,type="CNVtools")
mod<-CNVassoc(resp~CNV,data=simData,family="binomial")
CNVtest(mod)
summary(mod)

isglobal-brge/CNVassoc documentation built on May 30, 2019, 9:48 p.m.