Clonality (version 1.20.0)

ave.adj.probes: Averaging of adjacent probes in copy number arrays

Description

For each sample the log-ratios at each consecutive K number of probes are averaged.

Usage

ave.adj.probes(data, K)

Arguments

data

Copy Number Array object (output of function CNA() from the package DNAcopy). First column contains chromosomes, second column contains genomic locations. Each remaining column contains log-ratios from a particular tumor or sample.

K

Number of markers to be averaged. Should be selected so that the final resolution of the averaged data would be 5,000-10,000 markers.

Value

Returns CNA object of reduced resolution

Details

Averages log-ratios in every K consecutive markers. The purpose of this step is to reduce the noise in the data, eliminate possible very small germline copy number variations, and get rid of a possible wave effect.

Examples

# Same example as in clonality.analysis()

set.seed(100)
chrom<-rep(c(1:22),each=100)
maploc<- runif(2200)* 200000
chromarm<-splitChromosomes(chrom,maploc)
 
 
#Simulate the dataset with 10 pairs of tumors with 22 chromosomes, 100 markers each
#Simulated log-ratios are equal to signal + noise
#Signal: each chromosome has 50% chance to be normal, 30% to be whole-arm loss/gain, and 20% to be partial arm loss/gain, where endpoints are drawn at random, loss/gain means are drawn from normal distribution
#There are no chromosomes with recurrent losses/gains
#Noise: drawn from normal distribution with mean 0, standard deviation 0.25
#First 9 patients have independent tumors, last patient has two tumors with identical signal, independent noise


set.seed(100)
chrom<-paste("chr",rep(c(1:22),each=100),"p",sep="")
chrom[nchar(chrom)==5]<-paste("chr0",substr(chrom[nchar(chrom)==5] ,4,5),sep="")
maploc<- rep(c(1:100),22)
data<-NULL
for (pt in 1:9)  #first 9 patients have independent tumors
{
tumor1<-tumor2<- NULL
mean1<- rnorm(22) 
mean2<- rnorm(22)
for (chr in 1:22)
{ 
  r<-runif(2) 
if (r[1]<=0.5) tumor1<-c(tumor1,rep(0,100))   
  else if   (r[1]>0.7)  tumor1<-c(tumor1,rep(mean1[chr],100))
  else  { i<-sort(sample(1:100,2))
        tumor1<-c(tumor1,mean1[chr]*c(rep(0,  i[1]),rep(1, i[2]-i[1]), rep(0,  100-i[2])))
        }
if (r[2]<=0.5) tumor2<-c(tumor2,rep(0,100))
  else if   (r[2]>0.7)  tumor2<-c(tumor2,rep(mean2[chr],100))
  else   {i<-sort(sample(1:100,2))
       tumor2<-c(tumor2,mean2[chr]*c(rep(0,  i[1]),rep(1, i[2]-i[1]), rep(0,  100-i[2])))
         }
}
data<-cbind(data,tumor1,tumor2)
}

#last patient has identical profiles
tumor1<- NULL
mean1<- rnorm(22) 
for (chr in 1:22)
{ 
  r<-runif(1) 
if (r<=0.4) tumor1<-c(tumor1,rep(0,100))   
  else if   (r>0.6)  tumor1<-c(tumor1,rep(mean1[chr],100))
  else  { i<-sort(sample(1:100,2))
        tumor1<-c(tumor1,mean1[chr]*c(rep(0,  i[1]),rep(1, i[2]-i[1]), rep(0,  100-i[2])))
        }

}
data<-cbind(data,tumor1,tumor1)

data<-data+matrix(rnorm( 44000,mean=0,sd=0.4) ,nrow=2200,ncol=20)
dataCNA<-CNA(data,chrom=chrom,maploc=maploc,sampleid=paste("pt",rep(1:10,each=2),rep(1:2,10)))
dim(dataCNA)
dataCNA2<-ave.adj.probes(dataCNA, 2)
dim(dataCNA2)