groupBayesianCNVs: groupBayesianCNVs
In CNVrd2: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data.

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Cluster segmentation scores into different groups by using prior information from one population.

groupBayesianCNVs(xData, nGroups, lambda0, sd0, alpha0, distanceBetweenGroups, inits = NULL, 
		precisionOfGroupMeans = 3000, sdOftau = NULL, n.adapt = 100, 
		nUpdate = 1000, n.iter = 20000, thin = 5, n.chains = 1, 
		heidel.diag = FALSE, leftLimit = NULL, rightLimit = NULL)

`xData`	a numeric vector of observations (segmentation scores).
`nGroups`	an integer indicating a number of groups.
`lambda0`	Prior means of groups.
`sd0`	Prior standard deviations of groups.
`alpha0`	Prior parameters for mixing proportions.
`distanceBetweenGroups`	Prior value for the distance between groups.
`inits`	A list of initial values of parameters.
`precisionOfGroupMeans`	Prior parameter of group means (default = 3000).
`sdOftau`	Prior parameter of the standard deviations of group precisions.
`n.adapt`	the number of iterations for adaptation (`rjags`'s parameter)
`nUpdate`	the number of iterations for burn-in process.
`n.iter`	the number of iterations for sampling (`rjags`'s parameter)
`thin`	thinning interval for monitors (`rjags`'s parameter)
`n.chains`	the number of parallel chains for the model (`rjags`'s parameter, default = 1)
`heidel.diag`	If heidel.diag = TRUE then Heidelberger and Welch's convergence diagnostic is used.
`leftLimit`	Values which are less than this value will be allocated to the smallest group.
`rightLimit`	Values which are larger than this value will be allocated to the largest group.

This function assumes that users already know the information of groups' means, standard deviations; the distances between groups.

`mcmcChains`	A list of mcarray objects for means, standard deviations, proportions
`m1`	Means of groups
`s1`	Standard deviations of groups
`p1`	Proportions of groups
`allGroups`	A data.frame includes samples and their corresponding groups
`hTest`	Results of Heidelberger and Welch's convergence diagnostic

#####

Hoang Tan Nguyen, Tony R Merriman and MA Black. hoangtannguyenvn@gmail.com

Martyn Plummer (2013). rjags: Bayesian graphical models using MCMC. R package version 3-10. http://CRAN.R-project.org/package=rjags.

Lunn, David J., et al. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10.4 (2000): 325-337.

groupCNVs

## Not run: 
data(ccl3l1data)

xyEuro <- ccl3l1data[grep("CEU|TSI|IBS|GBR|FIN", ccl3l1data[, 2]), ]

names(yEuro) <- rownames(xyEuro)

##Clustering European segmentation scores into group: 5 groups were chosen

objectClusterEuroCCL3L1 <- new("clusteringCNVs", x = yEuro, k = 5)

europeanCCL3L1Groups <- groupCNVs(Object = objectClusterEuroCCL3L1)

##Obtain prior information
#Means
lambda0 <- as.numeric(europeanCCL3L1Groups$m)
#SD
sdEM <- as.numeric(europeanCCL3L1Groups$sigma)
#Proportions
pEM <- as.numeric(europeanCCL3L1Groups$p)


###Calculate the distances between groups
for (ii in 2:5){print(lambda0[ii] - lambda0[ii-1])}

###All segmentation scores
ccl3l1X <- ccl3l1data$SS
names(ccl3l1X) <- as.character(ccl3l1data$Name)
range(ccl3l1X)


 
##Set prior information: 
#prior for the sd of the means of groups: 
#5 was set for the third group = 2 CN
sd <- c(1, 1, 5, 1, 1) 
ccl3l1X <- sort(ccl3l1X)
###Data
xData <- ccl3l1X
###Number of groups
nGroups <- 10 
###prior for means of groups
lambda0 <- lambda0 
###Prior for mixing proportions
alpha0 <-  c(3, 29, 44, 18, 7,  5, rep(2, nGroups -length(pEM) -1))
##Prior for the distances between groups
distanceBetweenGroups = 0.485

sdEM = sdEM


##Adjust standard deviation for the fifth group
sdEM[5] <- sdEM[4]
 
 set.seed(123)
 groupCCL3L1allPops <- groupBayesianCNVs(xData = xData, nGroups = nGroups,
                                         lambda0 = lambda0,
                                         sd0 = sdEM, alpha0 = alpha0,
                                         distanceBetweenGroups = distanceBetweenGroups,
                                         sdOftau = sd,
                                        rightLimit = 4)




## End(Not run)