FACETS
facets is CNV detection software with using the ascn principle
input
first Let’s look at the test data given
Chromosome Chromosome of the SNP
Position Position of the SNP
File1R Read depth supporting the REF allele in normal sample
File1A Read depth supporting the ALT allele in normal sample
File2R Read depth supporting the REF allele in tumour sample
File2A Read depth supporting the ALT allele in tumour sample
The software comes with a comparison program, as shown in IGV
unlike other software ,facets calulates the depth of snp in each bin ,normal CNV software only calculates the depth of each bin then using CBS process.
next it Count whether the sum of ref and alt of each snp is heterozygous
the result is
summary
1R = REF-N
1A = ALT-N
2R = REF-T
2A = ALT-T
THE NEXT
1R=countN=N-RD
2R=countT=T-RD
N-DP=1R+1A
T-DP=2R+2A
varT=T-RD/T-DP
varN=N-RD/N-DP
as the picture shows
Then the software starts to calculate logR and logOR and gcbias the gcbias plaes look this blog http://www.zxzyl.com/archives/988
the logR is
ncount <- tapply(rCountN, gcpct, sum)
tcount <- tapply(rCountT, gcpct, sum)
tsc1=sum(ncount)/sum(tcount)
log2(1+rCountT*tscl) - log2(1+rCountN) - gcbias
the logOR is
1=varT x countT
2=(1-varT) x countT
3=varN x countN
4=(1-varN) x countN
than
## log-odds-ratio (Haldane correction)
logOR = log(1+0.5)-log(2+0.5)-log(3+0.5)+log(4+0.5) #using
## variance of log-odds-ratio (Haldane; Gart & Zweifel Biometrika 1967)
logOR = (1/( [,1]+0.5) + 1/( [,2]+0.5) + 1/( [,3]+0.5) + 1/( [,4]+0.5))
The following steps are the EM algorithm, here they use Bayesian in step E to get the posterior probability,
####LogR mixture model parameter####
gamma=2
phi=2*(1-rho)+gamma*rho
mu=log2(2*(1-rhov)+matrix(rhov,ncol=1)%*%t)-log2(phi)
####LogOR mixture model parameter####
#allelic ratio
k=(matrix(rhov,ncol=1)%*%major+1-rhov)/(matrix(rhov,ncol=1)%*%minor+1-rhov)
logk=log(k)
logk2=logk^2
#posterior probability matrix
#pmatrix=NULL
pmatrix=matrix(NA,nrow=nrow(jointseg),ncol=ng)
loglik=0
clust=rep(segclust,nmark)
segc=sort(unique(segclust[chr<=nX]))
for(s in segc){
idx=which(clust==s)
x1ij=logR.adj[idx]
upper=quantile(x1ij,0.95)
lower=quantile(x1ij,0.05)
x1ij[x1ij>upper]=NA
x1ij[x1ij mus=rep(mu[s,],each=length(idx)) sd=sigma[s] if(rhov[s]<0.4){ x1ij=rep(cnlr.median.clust[s]-dipLogR,length(idx)) sd=0.1 } #density for logR.adj (centered logR) d1=dnorm(x1ij,mean=mus,sd=sd) d1[d1==Inf]=NA #density for logOR, non-central chi-square nu=rep(logk2[s,],each=length(idx)) lambda=nu/rep(logORvar[idx],ng) x2ij=logOR2var[idx] if(rhov[s]<0.4){ x2ij=rep(mafR.clust[s]/logORvar.clust[s],length(idx)) lambda=nu/logORvar.clust[s] } #d2=dchisq(x2ij+1,df=1,ncp=lambda) d2=dchisq(x2ij,df=1,ncp=lambda) d2=1/(abs(x2ij-lambda)+1e-6) d2[d2==Inf]=NA #likelihood d=d1*d2 hetsum=d[rep(het[idx]==1,ng)] homsum=d1[rep(het[idx]==0,ng)] d=sum(hetsum[hetsum if(!is.na(d)&d>0&d #heterozygous positions contribute to logR and logOR numerator1=matrix(d1*d2,nrow=length(idx),ncol=ng,byrow=F) numerator1=sweep(numerator1,MARGIN=2,prior[s,],`*`) #homozygous positions contribute to logR only numerator0=matrix(d1,nrow=length(idx),ncol=ng,byrow=F) numerator0=sweep(numerator0,MARGIN=2,prior[s,],`*`) numerator=numerator1 numerator[het[idx]==0,]=numerator0[het[idx]==0,] tmp=apply(numerator,1,function(x)x/(sum(x,na.rm=T)+1e-5)) #pmatrix=rbind(pmatrix,t(tmp)) pmatrix[idx,]=t(tmp) #update prior prior[s,]=apply(t(tmp),2,function(x)mean(x,na.rm=T)) } the M step is #get CF per segments, pick mode close to 1 (favor high purity low cn solution) rhom=gammam=matrix(NA,nrow=nclust,ncol=ng) geno=matrix(0,nrow=nclust,ncol=ng) which.geno=posterior=rep(NA,nclust) for(i in segc){ idx=which(clust==i) idxhet=which(clust==i&het==1) sump=apply(pmatrix[idx,,drop=F],2,function(x)sum(x,na.rm=T)) #if probability is too small (highly uncertain), use lsd estimates for stability if(all(is.na(prior[i,]))){ prior[i,]=prior.old[i,] }else{ if(sum(prior[i,],na.rm=T)==0)prior[i,]=prior.old[i,] } if(max(prior[i,],na.rm=T)>0.05){ ##calculate rho for the most likely genotype(s) for segment i ##if there more more than one likely candidates save two and pick one with higher CF #top2=sort(prior[i,],decreasing=T)[1:2] #if(top2[2]>0.05&abs(diff(top2))<0.0001){ # sump[prior[i,] # }else{ # sump[prior[i,] # } sump[prior[i,] ##update k tmphet=pmatrix[idxhet,,drop=F] v1=as.vector((logOR[idxhet]^2-logORvar[idxhet])/logORvar[idxhet]) v2=as.vector(1/logORvar[idxhet]) sumdphet=apply(sweep(tmphet,MARGIN=1, v1, `*`), 2,function(x)sum(x,na.rm=T)) sumphet=apply(sweep(tmphet,MARGIN=1,v2,`*`), 2,function(x)sum(x,na.rm=T)) sumphet[is.na(sump)]=NA #CF from logOR logk2hat=pmax(0,sumdphet/sumphet) #can be negative when k=1 logk=0 set to 0 khat=exp(sqrt(logk2hat)) a=(1-khat)/(khat*(minor-1)-(major-1)) a[abs(a)==Inf]=NA a[a<=0]=NA a[a>1]=1 if(all(nhet[segclust==i] #CF from logR tmp=pmatrix[idx,,drop=F] v=as.vector(logR.adj[idx]) sumdp=apply(sweep(tmp,MARGIN=1,v,`*`),2,function(x)sum(x,na.rm=T)) mu.hat=sumdp/sump #mu.hat aa=2*(2^mu.hat-1)/(t-2) aa[abs(aa)==Inf]=NA aa[aa<=0]=NA aa[aa>1]=1 aaa=pmax(a,aa,na.rm=T) #degenerate cases #homozygous deletion (0) and balanced gain (AABB, AAABBB), maf=0.5, purity information comes from logr only aaa[c(1,8,13)]=aa[c(1,8,13)] #set upper bound at sample rho aaa=pmin(aaa,rho) #uniparental disomy (AA) CF information comes from logOR only. ##if there are two likely genotype, choose one with higher purity (e.g.,AAB 80% or AAAB 50%) ##if the higher CF exceeds sample purity, then the lower CF is the right one #if(all(is.na(aaa))){which.geno[i]=which.max(prior[i,])}else{ # which.geno[i]=ifelse(max(aaa,na.rm=T) #} which.geno[i]=which.max(prior[i,]) postprob=pmatrix[idx,which.geno[i]] posterior[i]=mean(postprob[postprob>0],na.rm=T) #update sigma y=as.vector(logR.adj[idx])*pmatrix[idx,,drop=F] r=y-mu[i,]*pmatrix[idx,,drop=F] ss=sqrt(sum(r[,which.geno[i]]^2,na.rm=T)/sum(pmatrix[idx,which.geno[i]])) sigma[i]=ifelse(is.na(ss),0.5,ss) aaa[setdiff(1:ng,which.geno[i])]=NA #het dip (AB) seg has no information, set CF at a high value less than 1 #if(any(which(!is.na(sump))==4)){aaa[4]=0.9} rhom[i,]=aaa } #max prior } Loop until it converges Or simpler, also use the CBS algorithm instead it 普通方式计算CNV 探针长度 chrom start end length chr1 12080 12251 172 chr1 12595 12802 208 normalized_coverage : for each target interval, the read depth (unique read starts) that correspond to a particular target interval is divided by the average number of read starts in all of the target intervals. normal chrom start end length normalized_coverage gene chr1 12080 12251 172 0 DDX11L1 chr1 12595 12802 208 0.002957 DDX11L1 tumor chrom start end length normalized_coverage gene chr1 12080 12251 172 0 DDX11L1 chr1 12595 12802 208 0.011583 DDX11L1 算法 例如gene EGFR 片段长度：length = L1 L2 L3 L4 L5 归一化覆盖:Tn = Tn1 Tn2 Tn3 Tn4 Tn5 Nn = Nn1 Nn2 Nn3 Nn4 Nn5 计算 : ((Tn1/Nn1)xL1+(Tn2/Nn2)xL2+·····)/L1+···L5 =拷贝率