CNVtools (version 1.66.0)

CNVtest.qt: Fits a mixture of Gaussian to CNV data

Description

This function fits a mixture of Gaussians to Copy Number Variant data to explore potential correlations between the copy number and a quantitative trait.

Usage

CNVtest.qt(signal, batch, sample = NULL, qt = NULL, ncomp, n.H0=5, n.H1=0, 
	   model.mean = '~ strata(cn)', model.var = '~ strata(cn)',
	   model.qt = '~ cn', beta.estimated = NULL, start.mean = NULL, start.var = NULL,
	   control=list(tol=1e-5, max.iter = 3000, min.freq=4) )

Arguments

signal

The vector of intensity values, meant to be a proxy for the number of copies.

batch

Factor, that describes how the data points should be separated in batches, corresponding to different tehnologies to measure the number of DNA copies, or maybe different cohorts in a case control framework.

sample

Optional (but recommended). A character vector containing a name for each data point, typically the name of the individuals.

qt

Quantitative trait values.

ncomp

Number of components one wants to fit to the data.

n.H0

Number of times the EM should be used to maximize the likelihood under the null hypothesis of no association, each time with a different random starting point. The run that maximizes the likelihood is stored.

n.H1

Number of times the EM should be used to maximize the likelihood under the alternate hypothesis of association present, each time with a different random starting point. The run that maximizes the likelihood is stored.

model.mean

Formula that relates the location of the means for the clusters with the number of copies and the different batches if there are multiple batches. The default is ``~ strata(cn)'' that assumes a free model for the cluster locations for each copy number.'' ~ strata(cn, batch)'' assumes free variances for each combination of copy number and batch. More traditional model specifications such as ' ~ cn' are also possible, but will converge more slowly and might have numerical stability issues.

model.var

A formula as above, but to model the variances. The default is the free variance model for each copy number ``~ strata(cn)'' and the same model specifications as model.means can be used.

model.qt

A formula that relates the number of copies with the case/control status. The default is a linear trend model ``~ cn''. Note that this formula will only matter under the alternate hypothesis and has no effect under the null.

beta.estimated

Optional. It is used if one wants to fit the model for a particular value of the log odds parameter beta (essentially if one is interested in the profile likelihood). In this case the disease model should be set to ' ~ 1' and the model to 'H1'. It will then provide the best model assuming the value of beta (the log odds ratio parameter) provided by the user.

start.mean

Optional. A set of starting values for the means. Must be numeric and the size must match ncomp.

start.var

Optional. A set of starting values for the variances. Must be numeric and the size must match ncomp.

control

A list of parameters that control the behavior of the fitting.

Value

model.H0

The parameters for the best fit under H0.

posterior.H0

The output dataframe with the estimate posterior distribution under H0 as well as the most likely call.

status.H0

A character that describes the status of the fit under H0. The possible values are 'C' (converged), 'M' (maximum iterations reached), 'P' (posterior distribution problem). Fits that don't return 'C' should be excluded.

model.H1

The parameters for the best fit under H1.

posterior.H1

The output dataframe with the estimate posterior distribution under H1

status.H1

A character that describes the status of the fit under H1. The possible values are 'C' (converged), 'M' (maximum iterations reached), 'P' (posterior distribution problem). Fits that don't return 'C' should be excluded.

See Also

apply.pca apply.ldf

Examples

	#Load data for CNV for two control cohorts 
	data(A112)
	raw.signal <- as.matrix(A112[, -c(1,2)])
	dimnames(raw.signal)[[1]] <- A112$subject

	#Extract CNV signal using principal components
	pca.signal <- apply.pca(raw.signal)

	#Extract batch, sample
	sample <- factor(A112$subject)
	batches <- rep("ALL",length(sample))

	#Create a fake quantitative trait
	trait <- rnorm(length(sample),mean=9.0,sd=1.0)

	#Fit the CNV with a three component model
	fit.pca <- CNVtest.qt(signal = pca.signal, sample = sample, batch = batches, 
		   	      qt = trait, ncomp = 3, n.H0=3, n.H1=3,
			      model.qt = "~ cn")
			  
	if(fit.pca[['status.H0']] == 'C' && fit.pca[['status.H1']] == 'C'){
	   #Calculate the likelihood ratio
	   LR <- -2*(fit.pca$model.H0$lnL - fit.pca$model.H1$lnL)			  
	
	   #Calculate the pvalue. Has 1 dof since we fit a trend model
	   pvalue <- 1 - pchisq(LR,1)
	}