ave.adj.probes {Clonality} | R Documentation

## 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.

### 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.

### Value

Returns CNA object of reduced resolution

### 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)

[Package Clonality version 1.40.0 Index]